Daily Papers

We have for the first time calculated the population characteristics of the Earth's irregular natural satellites (NES) that are temporarily captured from the near-Earth-object (NEO) population. The steady-state NES size-frequency and residence-time distributions were determined under the dynamical influence of all the massive bodies in the solar system (but mainly the Sun, Earth, and Moon) for NEOs of negligible mass. To this end, we compute the NES capture probability from the NEO population as a function of the latter's heliocentric orbital elements and combine those results with the current best estimates for the NEO size-frequency and orbital distribution. At any given time there should be at least one NES of 1-meter diameter orbiting the Earth. The average temporarily-captured orbiter (TCO; an object that makes at least one revolution around the Earth in a co-rotating coordinate system) completes (2.88pm0.82)rev around the Earth during a capture event that lasts (286pm18)days. We find a small preference for capture events starting in either January or July. Our results are consistent with the single known natural TCO, 2006 RH_{120}, a few-meter diameter object that was captured for about a year starting in June 2006. We estimate that about 0.1% of all meteors impacting the Earth were TCOs.

Fast moving celestial objects are characterized by velocities across the celestial sphere that significantly differ from the motions of background stars. In observational images, these objects exhibit distinct shapes, contrasting with the typical appearances of stars. Depending on the observational method employed, these celestial entities may be designated as near-Earth objects or asteroids. Historically, fast moving celestial objects have been observed using ground-based telescopes, where the relative stability of stars and Earth facilitated effective image differencing techniques alongside traditional fast moving celestial object detection and classification algorithms. However, the growing prevalence of space-based telescopes, along with their diverse observational modes, produces images with different properties, rendering conventional methods less effective. This paper presents a novel algorithm for detecting fast moving celestial objects within star fields. Our approach enhances state-of-the-art fast moving celestial object detection neural networks by transforming them into physical-inspired neural networks. These neural networks leverage the point spread function of the telescope and the specific observational mode as prior information; they can directly identify moving fast moving celestial objects within star fields without requiring additional training, thereby addressing the limitations of traditional techniques. Additionally, all neural networks are integrated using the mixture of experts technique, forming a comprehensive fast moving celestial object detection algorithm. We have evaluated our algorithm using simulated observational data that mimics various observations carried out by space based telescope scenarios and real observation images. Results demonstrate that our method effectively detects fast moving celestial objects across different observational modes.

We report the first scientific results from the NELIOTA (NEO Lunar Impacts and Optical TrAnsients) project, which has recently begun lunar monitoring observations with the 1.2-m Kryoneri telescope. NELIOTA aims to detect faint impact flashes produced by near-Earth meteoroids and asteroids and thereby help constrain the size-frequency distribution of near-Earth objects in the decimeter to meter range. The NELIOTA setup, consisting of two fast-frame cameras observing simultaneously in the R and I-bands, enables - for the first time - direct analytical calculation of the flash temperatures. We present the first 10 flashes detected, for which we find temperatures in the range ~1,600-3,100 K, in agreement with theoretical values. Two of these flashes were detected on multiple frames in both filters and therefore yield the first measurements of the temperature drop for lunar flashes. In addition, we compute the impactor masses, which range between ~100 g and ~50 kg.

NELIOTA is a new ESA activity launched at the National Observatory of Athens in February 2015 aiming to determine the distribution and frequency of small near-earth objects (NEOs) via lunar monitoring. The project involves upgrading the 1.2m Kryoneri telescope at the National Observatory of Athens, procuring two fast-frame cameras, and developing a software system, which will control the telescope and the cameras, process the images and automatically detect NEO impacts. NELIOTA will provide a web-based user interface, where the impact events will be reported and made available to the scientific community and the general public. The objective of this 3.5 year activity is to design, develop and implement a highly automated lunar monitoring system, which will conduct an observing campaign for 2 years in search of NEO impact flashes on the Moon. The impact events will be verified, characterised and reported. The 1.2m telescope will be capable of detecting flashes much fainter than current, small-aperture, lunar monitoring telescopes. NELIOTA is therefore expected to characterise the frequency and distribution of NEOs weighing as little as a few grams.

We present the technical specifications and first results of the ESA-funded, lunar monitoring project "NELIOTA" (NEO Lunar Impacts and Optical TrAnsients) at the National Observatory of Athens, which aims to determine the size-frequency distribution of small Near-Earth Objects (NEOs) via detection of impact flashes on the surface of the Moon. For the purposes of this project a twin camera instrument was specially designed and installed at the 1.2 m Kryoneri telescope utilizing the fast-frame capabilities of scientific Complementary Metal-Oxide Semiconductor detectors (sCMOS). The system provides a wide field-of-view (17.0' times 14.4') and simultaneous observations in two photometric bands (R and I), reaching limiting magnitudes of 18.7 mag in 10 sec in both bands at a 2.5 signal-to-noise level. This makes it a unique instrument that can be used for the detection of NEO impacts on the Moon, as well as for any astronomy projects that demand high-cadence multicolor observations. The wide field-of-view ensures that a large portion of the Moon is observed, while the simultaneous, high-cadence, monitoring in two photometric bands makes possible, for the first time, the determination of the temperatures of the impacts on the Moon's surface and the validation of the impact flashes from a single site. Considering the varying background level on the Moon's surface we demonstrate that the NELIOTA system can detect NEO impact flashes at a 2.5 signal-to-noise level of ~12.4 mag in the I-band and R-band for observations made at low lunar phases ~0.1. We report 31 NEO impact flashes detected during the first year of the NELIOTA campaign. The faintest flash was at 11.24 mag in the R-band (about two magnitudes fainter than ever observed before) at lunar phase 0.32. Our observations suggest a detection rate of 1.96 times 10^{-7} events km^{-2} h^{-1}.

The near-Earth asteroid 2024 YR4 -- a sim60 m rocky object that was once considered a potential Earth impactor -- has since been ruled out for Earth but retained a sim4.3% probability of striking the Moon in 2032. Such an impact, with equivalent kinetic energy of sim6.5 Mt TNT, is expected to produce a sim1 km crater on the Moon, and will be the most energetic lunar impact event ever recorded in human history. Despite the associated risk, this scenario offers a rare and valuable scientific opportunity. Using a hybrid framework combining Monte Carlo orbital propagation, smoothed particle hydrodynamics (SPH) impact modeling, and N-body ejecta dynamics, we evaluate the physical outcomes and propose the observation timelines of this rare event. Our results suggest an optical flash of visual magnitude from -2.5 to -3 lasting several minutes directly after the impact, followed by hours of infrared afterglow from sim2000 K molten rock cooling to a few hundred K. The associated seismic energy release would lead to a global-scale lunar reverberation (magnitude sim5.0) that can be detectable by modern seismometers. Furthermore, the impact would eject sim10^8 kg of debris that escapes the lunar gravity, with a small fraction reaching Earth to produce a lunar meteor outburst within 100 years. Finally, we integrate these results into a coordinated observation timeline, identifying the best detection windows for ground-based telescopes, lunar orbiters, and surface stations.

We present the results from four stellar occultations by (486958) Arrokoth, the flyby target of the New Horizons extended mission. Three of the four efforts led to positive detections of the body, and all constrained the presence of rings and other debris, finding none. Twenty-five mobile stations were deployed for 2017 June 3 and augmented by fixed telescopes. There were no positive detections from this effort. The event on 2017 July 10 was observed by SOFIA with one very short chord. Twenty-four deployed stations on 2017 July 17 resulted in five chords that clearly showed a complicated shape consistent with a contact binary with rough dimensions of 20 by 30 km for the overall outline. A visible albedo of 10% was derived from these data. Twenty-two systems were deployed for the fourth event on 2018 Aug 4 and resulted in two chords. The combination of the occultation data and the flyby results provides a significant refinement of the rotation period, now estimated to be 15.9380 pm 0.0005 hours. The occultation data also provided high-precision astrometric constraints on the position of the object that were crucial for supporting the navigation for the New Horizons flyby. This work demonstrates an effective method for obtaining detailed size and shape information and probing for rings and dust on distant Kuiper Belt objects as well as being an important source of positional data that can aid in spacecraft navigation that is particularly useful for small and distant bodies.

Motivated by the peculiar features of the trans-Neptunian object (TNO) Máni and the discovery of ring systems around Centaurs and trans-Neptunian bodies, we investigate the stability of particles around a small irregular body hosting a deep equatorial crater. This study is the third contribution in a sequence devoted to the dynamics around non-spherically symmetric bodies. Using Máni as a reference model, whose crater depth exceeds 10% of its radius, we map the system stability through complementary methods: Poincaré surfaces of section (PSS), survival maps, and the finite-time Lyapunov exponent (FTLE). In the nominal case, the 1!:!1, 2!:!1, 3!:!1, and 4!:!1 spin-orbit resonances (SORs) are consistently identified by all techniques. The prominent 3!:!1 SOR exhibits a bifurcated structure at high eccentricities, matching structural transitions observed at lower Jacobi constant values in the PSSs. Increasing the rotation rate (λ) and crater mass ratio (μ) enlarges resonance widths and promotes overlap, making the 2!:!1 and 4!:!1 SORs progressively dominant. Lower rotation rates allow stable trajectories to persist at higher eccentricities, whereas the 1!:!1 SOR is highly sensitive to stronger perturbations and disappears in the most extreme cases. In contrast to mass-anomaly models, which can clear the region interior to the 2!:!1 SOR, the crater configuration considered here preserves stable regions in this vicinity, suggesting alternative scenarios for the formation and maintenance of debris structures around irregular minor bodies.

The coldest Y spectral type brown dwarfs are similar in mass and temperature to cool and warm (sim200 -- 400 K) giant exoplanets. We can therefore use their atmospheres as proxies for planetary atmospheres, testing our understanding of physics and chemistry for these complex, cool worlds. At these cold temperatures, their atmospheres are cold enough for water clouds to form, and chemical timescales increase, increasing the likelihood of disequilibrium chemistry compared to warmer classes of planets. JWST observations are revolutionizing the characterization of these worlds with high signal-to-noise, moderate resolution near- and mid-infrared spectra. The spectra have been used to measure the abundances of prominent species like water, methane, and ammonia; species that trace chemical reactions like carbon monoxide; and even isotopologues of carbon monoxide and ammonia. Here, we present atmospheric retrieval results using both published fixed-slit (GTO program 1230) and new averaged time series observations (GO program 2327) of the coldest known Y dwarf, WISE 0855-0714 (using NIRSpec G395M spectra), which has an effective temperature of sim 264 K. We present a detection of deuterium in an atmosphere outside of the solar system via a relative measurement of deuterated methane (CH_{3}D) and standard methane. From this, we infer the D/H ratio of a substellar object outside the solar system for the first time. We also present a well-constrained part-per-billion abundance of phosphine (PH_{3}). We discuss our interpretation of these results and the implications for brown dwarf and giant exoplanet formation and evolution.

Mergers play a complex role in galaxy formation and evolution. Continuing to improve our understanding of these systems require ever larger samples, which can be difficult (even impossible) to select from individual surveys. We use the new platform ESA Datalabs to assemble a catalogue of interacting galaxies from the Hubble Space Telescope science archives; this catalogue is larger than previously published catalogues by nearly an order of magnitude. In particular, we apply the Zoobot convolutional neural network directly to the entire public archive of HST F814W images and make probabilistic interaction predictions for 126 million sources from the Hubble Source Catalogue. We employ a combination of automated visual representation and visual analysis to identify a clean sample of 21,926 interacting galaxy systems, mostly with z < 1. Sixty five percent of these systems have no previous references in either the NASA Extragalactic Database or Simbad. In the process of removing contamination, we also discover many other objects of interest, such as gravitational lenses, edge-on protoplanetary disks, and `backlit' overlapping galaxies. We briefly investigate the basic properties of this sample, and we make our catalogue publicly available for use by the community. In addition to providing a new catalogue of scientifically interesting objects imaged by HST, this work also demonstrates the power of the ESA Datalabs tool to facilitate substantial archival analysis without placing a high computational or storage burden on the end user.

We have conducted an extensive towed-magnetic-sled survey during the period of June 14-28, 2023, over the seafloor about 85 km north of Manus Island, Papua New Guinea, centered around the calculated path of the bolide CNEOS 2014-01-08 (IM1). We found about 850 spherules of diameter 0.1-1.3 millimeters in our samples. The samples were analyzed by micro-XRF, Electron Probe Microanalyzer and ICP Mass spectrometry. Here we report major and trace element compositions of the samples and classify spherules based on that analysis. Spherules comprising 22% of the collection, appear to all reflect planetary igneous differentiation and are all different from previously described spherules. A subset of the differentiated-spherules show an excess of Be, La and U, by up to three orders of magnitude relative to the solar system standard of CI chondrites. Detailed mass spectrometry of 12 of these "BeLaU"-type spherules, the population of which may constitute up to ~10% of our entire collected sample, suggests that they are derived from material formed by planetary igneous fractionation. Their chemical composition is unlike any known solar system material. The "BeLaU"-type spherules reflect a highly differentiated, extremely evolved composition of an unknown source.

Context. Three kilometer-sized interstellar objects (ISOs) have been detected transiting the Solar System, and spacecraft have directly measured micrometer-scale interstellar dust (ISD). Yet no intermediate-size interstellar meteoroids have been identified in current meteor surveys. Aims. We test whether a power-law flux extrapolation connecting spacecraft ISD and kilometer-scale ISOs is consistent with meteor surveys, and we quantify the expected interstellar impacting flux based on various observational reports. Methods. We compiled differential fluxes and limits from spacecraft ISD, radar and optical meteor surveys, and theoretical estimates. We evaluated the power-law size-frequency fits, computed the 3I-like flux, and compared measured fluxes to predictions. Results. The spacecraft-measured dust flux exceeds extrapolations constrained by meteor surveys and kilometer-scale ISOs by sim2-7 orders of magnitude. An r^{-3.0} fit combining spacecraft ISD detections with kilometer-scale ISOs overpredicts the number of meteors with hyperbolic orbits, whereas slopes of r^{-2.7}-r^{-2.3} (derived from radar and optical meteor upper limits, respectively) instead yield interplanetary-to-interstellar flux ratios of 10^{3}-10^{6}. Conclusions. A simple power-law from ISD to ISOs is inconsistent with meteor survey constraints and yields unrealistic predictions for interstellar meteoroids. The data reveal a gap between submicron dust entrained in the Local Interstellar Cloud (LIC) and macroscopic bodies ejected from planetary systems. This gap may reflect distinct origins and destruction-transport processes rather than a continuous size-frequency distribution. This would imply either the dominance of a small-particle LIC component or the need to reassess spacecraft dust fluxes.

We observed the dynamically similar near-Sun asteroids 2021 PH27 and 2025 GN1 for their optical colors. These objects have the lowest known semi-major axes of any asteroids. 2021 PH27 has the largest general relativistic effects of any known solar system object. The small semi-major axis and very close passage to the Sun suggests the extreme thermal and gravitational environment should highly modify these asteroids' surfaces. From g', r', i' and z'-band imaging, we find the colors of 2021 PH27 to be between the two major asteroid types the S and C classes (g'-r'= 0.58 +- 0.02, r'-i'=0.12 +- 0.02 and i'-z'=-0.08 +- 0.05 mags). With a spectral slope of 6.8 +-0.03 percent per 100nm, 2021 PH27 is a X-type asteroid and requires albedo or spectral features to further identify its composition. We find the dynamically similar 2025 GN1 also has very similar colors (g'-r'=0.55 +-0.06 and r'-i'=0.14 +-0.04) as 2021 PH27, suggesting these objects are fragments from a once larger parent asteroid or 2021 PH27 is shedding material. The colors are not blue like some other near-Sun asteroids such as 3200 Phaethon that have been interpreted to be from the loss of reddening substances from the extreme temperatures. There is no evidence of activity or a large amplitude period for 2021 PH27, whereas 2025 GN1 might have a more significant rotational light curve. 2025 GN1 may have a very close encounter or hit Venus in about 2155 years and likely separated from 2021 PH27 in about the last 10 kyrs.

This paper contains the results from the first 30 months of the NELIOTA project for impacts of NEOs/meteoroids on the lunar surface. Our analysis on the statistics concerning the efficiency of the campaign and the parameters of the projectiles and the impacts is presented. The parameters of the lunar impact flashes based on simultaneous observations in two wavelength bands are used to estimate the distributions of the masses, sizes and frequency of the impactors. These statistics can be used both in space engineering and science. The photometric fluxes of the flashes are measured using aperture photometry and their apparent magnitudes are calculated using standard stars. Assuming that the flashes follow a black body law of irradiation, the temperatures can be derived analytically, while the parameters of the projectiles are estimated using fair assumptions on their velocity and luminous efficiency of the impacts. 79 lunar impact flashes have been observed with the 1.2 m Kryoneri telescope in Greece. The masses of the meteoroids range between 0.7 g and 8 kg and their respective sizes between 1-20 cm depending on their assumed density, impact velocity, and luminous efficiency. We find a strong correlation between the observed magnitudes of the flashes and the masses of the meteoroids. Moreover, an empirical relation between the emitted energies of each band has been derived allowing the estimation of the physical parameters of the meteoroids that produce low energy impact flashes. The NELIOTA project has so far the highest detection rate and the faintest limiting magnitude for lunar impacts compared to other ongoing programs. Based on the impact frequency distribution on Moon, we estimate that sporadic meteoroids with typical masses less than 100 g and sizes less than 5 cm enter the mesosphere of the Earth with a rate ~108 meteoroids/hr and also impact Moon with a rate of ~8 meteoroids/hr.

We describe the design and performance of the near-infrared (1.51--1.70 micron), fiber-fed, multi-object (300 fibers), high resolution (R = lambda/delta lambda ~ 22,500) spectrograph built for the Apache Point Observatory Galactic Evolution Experiment (APOGEE). APOGEE is a survey of ~ 10^5 red giant stars that systematically sampled all Milky Way populations (bulge, disk, and halo) to study the Galaxy's chemical and kinematical history. It was part of the Sloan Digital Sky Survey III (SDSS-III) from 2011 -- 2014 using the 2.5 m Sloan Foundation Telescope at Apache Point Observatory, New Mexico. The APOGEE-2 survey is now using the spectrograph as part of SDSS-IV, as well as a second spectrograph, a close copy of the first, operating at the 2.5 m du Pont Telescope at Las Campanas Observatory in Chile. Although several fiber-fed, multi-object, high resolution spectrographs have been built for visual wavelength spectroscopy, the APOGEE spectrograph is one of the first such instruments built for observations in the near-infrared. The instrument's successful development was enabled by several key innovations, including a "gang connector" to allow simultaneous connections of 300 fibers; hermetically sealed feedthroughs to allow fibers to pass through the cryostat wall continuously; the first cryogenically deployed mosaic volume phase holographic grating; and a large refractive camera that includes mono-crystalline silicon and fused silica elements with diameters as large as ~ 400 mm. This paper contains a comprehensive description of all aspects of the instrument including the fiber system, optics and opto-mechanics, detector arrays, mechanics and cryogenics, instrument control, calibration system, optical performance and stability, lessons learned, and design changes for the second instrument.

Recent space-borne and ground-based observations provide photometric measurements as time series. The effect of interstellar dust extinction in the near-infrared range is only 10% of that measured in the V band. However, the sensitivity of the light curve shape to the physical parameters in the near-infrared is much lower. So, interpreting these types of data sets requires new approaches like the different large-scale surveys, which create similar problems with big data. Using a selected data set, we provide a method for applying routines implemented in R to extract most information of measurements to determine physical parameters, which can also be used in automatic classification schemes and pipeline processing. We made a multivariate classification of 131 Cepheid light curves (LC) in J, H, and K colors, where all the LCs were represented in 20D parameter space in these colors separately. Performing a Principal Component Analysis (PCA), we got an orthogonal coordinate system and squared Euclidean distances between LCs, with 6 significant eigenvalues, reducing the 20-dimension to 6. We also estimated the optimal number of partitions of similar objects and found it to be equal to 7 in each color; their dependence on the period, absolute magnitude, amplitude, and metallicity are also discussed. We computed the Spearman rank correlations, showing that periods and absolute magnitudes correlate with the first three PCs significantly. The first two PC are also found to have a relationship with the amplitude, but the metallicity effects are only marginal. The method shown can be generalized and implemented in unsupervised classification schemes and analysis of mixed and biased samples. The analysis of our Classical Cepheid near-infrared LC sample showed that the J, H, K curves are insufficient for determination of stellar metallicity, with mass being the key factor shaping them.

TOI-836 is a ~2-3 Gyr K dwarf with an inner super Earth (R=1.7 R_oplus, P=3.8 d) and an outer mini Neptune (R=2.6 R_oplus, P=8.6 d). JWST/NIRSpec 2.8--5.2 mum transmission spectra are flat for both planets. We present Keck/NIRSPEC observations of escaping helium for super-Earth b, which shows no excess absorption in the 1083 nm triplet to deep limits (<0.2%), and mini-Neptune c, which shows strong (0.7%) excess absorption in both visits. These results demonstrate that planet c retains at least some primordial atmosphere, while planet b is consistent with having lost its entire primordial envelope. Self-consistent 1D radiative-hydrodynamic models of planet c reveal that the helium excess absorption signal is highly sensitive to metallicity: its equivalent width collapses by a factor of 13 as metallicity increases from 10x to 100x solar, and by a further factor of 12 as it increases to 200x solar. The observed equivalent width is 88\% the model prediction for 100x metallicity, suggesting an atmospheric metallicity similar to K2-18b and TOI-270d, the first two mini-Neptunes with detected absorption features in JWST transmission spectra. We highlight the helium triplet as a potentially powerful probe of atmospheric composition, with complementary strengths and weaknesses to atmospheric retrievals. The main strength is its extreme sensitivity to metallicity in the scientifically significant range of 10--200x solar, and the main weakness is the enormous model uncertainties in outflow suppression and confinement mechanisms, such as magnetic fields and stellar winds, which can suppress the signal by at least a factor of ~several.

Astronomical archives contain vast quantities of unexplored data that potentially harbour rare and scientifically valuable cosmic phenomena. We leverage new semi-supervised methods to extract such objects from the Hubble Legacy Archive. We have systematically searched approximately 100 million image cutouts from the entire Hubble Legacy Archive using the recently developed AnomalyMatch method, which combines semi-supervised and active learning techniques for the efficient detection of astrophysical anomalies. This comprehensive search rapidly uncovered a multitude of astrophysical anomalies presented here that significantly expand the inventory of known rare objects. Among our discoveries are 138 new candidate gravitational lenses, 18 jellyfish galaxies, and 417 mergers or interacting galaxies. The efficiency and accuracy of our iterative detection strategy allows us to trawl the complete archive within just 2-3 days, highlighting its potential for large-scale astronomical surveys. We present a detailed overview of these newly identified objects, discuss their astrophysical significance, and demonstrate the considerable potential of AnomalyMatch to efficiently explore extensive astronomical datasets, including, e.g., upcoming Euclid data releases.

We present final natural system optical (ugriBV) and near-infrared (YJH) photometry of 134 supernovae (SNe) with probable white dwarf progenitors that were observed in 2004-2009 as part of the first stage of the Carnegie Supernova Project (CSP-I). The sample consists of 123 Type Ia SNe, 5 Type Iax SNe, 2 super-Chandrasekhar SN candidates, 2 Type Ia SNe interacting with circumstellar matter, and 2 SN 2006bt-like events. The redshifts of the objects range from z = 0.0037 to 0.0835; the median redshift is 0.0241. For 120 (90%) of these SNe, near-infrared photometry was obtained. Average optical extinction coefficients and color terms are derived and demonstrated to be stable during the five CSP-I observing campaigns. Measurements of the CSP-I near-infrared bandpasses are also described, and near-infrared color terms are estimated through synthetic photometry of stellar atmosphere models. Optical and near-infrared magnitudes of local sequences of tertiary standard stars for each supernova are given, and a new calibration of Y-band magnitudes of the Persson et al. (1998) standards in the CSP-I natural system is presented.

We present PAPERCLIP (Proposal Abstracts Provide an Effective Representation for Contrastive Language-Image Pre-training), a method which associates astronomical observations imaged by telescopes with natural language using a neural network model. The model is fine-tuned from a pre-trained Contrastive Language-Image Pre-training (CLIP) model using successful observing proposal abstracts and corresponding downstream observations, with the abstracts optionally summarized via guided generation using large language models (LLMs). Using observations from the Hubble Space Telescope (HST) as an example, we show that the fine-tuned model embodies a meaningful joint representation between observations and natural language through tests targeting image retrieval (i.e., finding the most relevant observations using natural language queries) and description retrieval (i.e., querying for astrophysical object classes and use cases most relevant to a given observation). Our study demonstrates the potential for using generalist foundation models rather than task-specific models for interacting with astronomical data by leveraging text as an interface.

Over the past decade, the study of extrasolar planets has evolved rapidly from plain detection and identification to comprehensive categorization and characterization of exoplanet systems and their atmospheres. Atmospheric retrieval, the inverse modeling technique used to determine an exoplanetary atmosphere's temperature structure and composition from an observed spectrum, is both time-consuming and compute-intensive, requiring complex algorithms that compare thousands to millions of atmospheric models to the observational data to find the most probable values and associated uncertainties for each model parameter. For rocky, terrestrial planets, the retrieved atmospheric composition can give insight into the surface fluxes of gaseous species necessary to maintain the stability of that atmosphere, which may in turn provide insight into the geological and/or biological processes active on the planet. These atmospheres contain many molecules, some of them biosignatures, spectral fingerprints indicative of biological activity, which will become observable with the next generation of telescopes. Runtimes of traditional retrieval models scale with the number of model parameters, so as more molecular species are considered, runtimes can become prohibitively long. Recent advances in machine learning (ML) and computer vision offer new ways to reduce the time to perform a retrieval by orders of magnitude, given a sufficient data set to train with. Here we present an ML-based retrieval framework called Intelligent exoplaNet Atmospheric RetrievAl (INARA) that consists of a Bayesian deep learning model for retrieval and a data set of 3,000,000 synthetic rocky exoplanetary spectra generated using the NASA Planetary Spectrum Generator. Our work represents the first ML retrieval model for rocky, terrestrial exoplanets and the first synthetic data set of terrestrial spectra generated at this scale.

Half of the JWST high-contrast imaging objects will only have photometric data {{as of Cycle 2}}. However, to better understand their atmospheric chemistry which informs formation origin, spectroscopic data are preferred. Using HIP 65426 b, we investigate to what extent planet properties and atmospheric chemical abundance can be retrieved with only JWST photometric data points (2.5-15.5 mum) in conjunction with ground-based archival low-resolution spectral data (1.0-2.3 mum). We find that the data is consistent with an atmosphere with solar metallicity and C/O ratios at 0.40 and 0.55. We rule out 10x solar metallicity and an atmosphere with C/O = 1.0. We also find strong evidence of silicate clouds but no sign of an enshrouding featureless {{dust}} extinction. This work offers guidance and cautionary tales on analyzing data in the absence of medium-to-high resolution spectral data.

We present results of the NELIOTA campaign for lunar impact flashes observed with the 1.2 m Kryoneri telescope. From August 2019 to August 2023, we report 113 validated and 70 suspected flashes. For the validated flashes, we calculate the physical parameters of the corresponding projectiles, the temperatures developed during the impacts, and the expected crater sizes. For the multiframe flashes we present light curves and thermal evolution plots. Using the whole sample of NELIOTA that encompasses 192 validated flashes in total from 2017, the statistics of the physical parameters of the meteoroids, the peak temperatures of the impacts and the expected crater sizes has been updated. Using this large sample, empirical relations correlating the luminous energies per photometric band were derived and used to roughly estimate the parameters of 92 suspected flashes of the NELIOTA archive. For a typical value of the luminous efficiency, we found that more than the 75% of the impacting meteoroids have masses between 1-200 g, radii between 0.5-3 cm and produced craters up to 3.5 m. 85% of the peak temperatures of the impacts range between 2000 and 4500 K. Statistics regarding the magnitude decline and the cooling rates of the multiframe flashes are also presented. The recalculation of the appearance frequency of meteoroids, lying within the aforementioned ranges of physical parameters, on the Moon yields that the total lunar surface is bombarded with 7.4 sporadic meteoroids/ hour and up to 12.6 meteoroids/hour when the Earth-Moon system passes through a strong meteoroid stream. By extrapolating these rates on Earth, the respective rates for various distances from its surface are calculated and used to estimate the probability of an impact of a meteoroid with a hypothetical infrastructure on the Moon, or with a satellite orbiting Earth for various impact surfaces and duration times of the missions.

Novel view synthesis (NVS) enables to generate new images of a scene or convert a set of 2D images into a comprehensive 3D model. In the context of Space Domain Awareness, since space is becoming increasingly congested, NVS can accurately map space objects and debris, improving the safety and efficiency of space operations. Similarly, in Rendezvous and Proximity Operations missions, 3D models can provide details about a target object's shape, size, and orientation, allowing for better planning and prediction of the target's behavior. In this work, we explore the generalization abilities of these reconstruction techniques, aiming to avoid the necessity of retraining for each new scene, by presenting a novel approach to 3D spacecraft reconstruction from single-view images, DreamSat, by fine-tuning the Zero123 XL, a state-of-the-art single-view reconstruction model, on a high-quality dataset of 190 high-quality spacecraft models and integrating it into the DreamGaussian framework. We demonstrate consistent improvements in reconstruction quality across multiple metrics, including Contrastive Language-Image Pretraining (CLIP) score (+0.33%), Peak Signal-to-Noise Ratio (PSNR) (+2.53%), Structural Similarity Index (SSIM) (+2.38%), and Learned Perceptual Image Patch Similarity (LPIPS) (+0.16%) on a test set of 30 previously unseen spacecraft images. Our method addresses the lack of domain-specific 3D reconstruction tools in the space industry by leveraging state-of-the-art diffusion models and 3D Gaussian splatting techniques. This approach maintains the efficiency of the DreamGaussian framework while enhancing the accuracy and detail of spacecraft reconstructions. The code for this work can be accessed on GitHub (https://github.com/ARCLab-MIT/space-nvs).

The continuous operation of Earth-orbiting satellites generates vast and ever-growing archives of Remote Sensing (RS) images. Natural language presents an intuitive interface for accessing, querying, and interpreting the data from such archives. However, existing Vision-Language Models (VLMs) are predominantly trained on web-scraped, noisy image-text data, exhibiting limited exposure to the specialized domain of RS. This deficiency results in poor performance on RS-specific tasks, as commonly used datasets often lack detailed, scientifically accurate textual descriptions and instead emphasize solely on attributes like date and location. To bridge this critical gap, we introduce GAIA, a novel dataset designed for multi-scale, multi-sensor, and multi-modal RS image analysis. GAIA comprises of 205,150 meticulously curated RS image-text pairs, representing a diverse range of RS modalities associated to different spatial resolutions. Unlike existing vision-language datasets in RS, GAIA specifically focuses on capturing a diverse range of RS applications, providing unique information about environmental changes, natural disasters, and various other dynamic phenomena. The dataset provides a spatially and temporally balanced distribution, spanning across the globe, covering the last 25 years with a balanced temporal distribution of observations. GAIA's construction involved a two-stage process: (1) targeted web-scraping of images and accompanying text from reputable RS-related sources, and (2) generation of five high-quality, scientifically grounded synthetic captions for each image using carefully crafted prompts that leverage the advanced vision-language capabilities of GPT-4o. Our extensive experiments, including fine-tuning of CLIP and BLIP2 models, demonstrate that GAIA significantly improves performance on RS image classification, cross-modal retrieval and image captioning tasks.

As orbital populations grow, conjunction screening must search more object pairs for potential collision risks. Existing pipelines use early-stage filters, such as an apsis filter, to eliminate pairs whose orbital altitude ranges cannot produce a close approach. However, these filters apply the same fixed threshold to every object, making the screening unnecessarily conservative for well-constrained objects while potentially insufficient for objects with greater orbit-prediction uncertainty. In this work, we present an uncertainty-aware conjunction screening method that replaces a single fixed apsis threshold with object-specific thresholds derived from propagated radial uncertainty. The resulting thresholds vary across both objects and prediction time, becoming narrower when propagated uncertainty is small and wider when uncertainty grows. We pair this adaptive screening rule with a binary-search altitude sweep that makes resulting heterogeneous intervals efficient to evaluate at catalog scale with log-linear search complexity. We evaluate the method on 33,791 objects from the Space-Track General Perturbations (GP) catalog using the preceding eight public ephemeris snapshots and a seven-day prediction horizon. The adaptive sieve retains 162.8 million candidate pairs, reducing the candidate set by 28.78% relative to a fixed 50 km apsis threshold and by 71.48% relative to brute-force all-pairs screening. It retains all close approaches identified in two independently propagated reference sets. At the largest tested catalog size, the binary-search sweep is 27 times faster than exhaustive interval evaluation while returning the same candidate set. These results demonstrate that uncertainty-aware screening can substantially reduce the candidate burden passed to downstream conjunction analysis, providing a scalable path toward managing increasingly large and heterogeneous orbital populations.

We describe pre-perihelion optical observations of interstellar comet 3I/ATLAS taken during July - September 2025 using the Nordic Optical Telescope. Fixed aperture photometry of the comet is well described by a power law function of heliocentric distance, rH, with the exponent (``index") n = 3.8+/-0.3 across the 4.6 au to 1.8 au distance range (phase function 0.04+/-0.02 magnitude/degree assumed). This indicates that the dust production rates vary in proportion to rH**(-1.8+/-0.3). An rH**(-2) variation is expected of a strongly volatile material, and consistent with independent spectroscopic observations showing that carbon dioxide is the primary driver of activity. The measured heliocentric index is unremarkable in the context of solar system comets, for which n is widely dispersed, and provides no basis on which to describe 3I as either dynamically old (thermally processed) or new (pristine). The morphology of the comet changes from a Sun-facing dust fan in the early 2025 July observations, to one dominated by an antisolar dust tail at later dates. We attribute the delayed emergence of the tail to the large size (effective radius 0.1 mm) and slow ejection (5 m/s) of the optically dominant dust particles, and their consequently sluggish response to solar radiation pressure. Small (micron-sized) particles may be present but not in numbers sufficient to dominate the scattering cross-section. Their relative depletion possibly reflects interparticle cohesion, which binds small particles more effectively than large ones. A similar preponderance of 0.1 mm grains was reported in 2I/Borisov. However, 2I differed from 3I in having a much smaller (asteroid-like) heliocentric index, n = 1.9+/-0.1. Dust production rates in 3I are 180 kg/s at 2 au, compared with 70 kg/s in 2I/Borisov at the same distance.

In our search for life beyond the Solar System, certain planetary bodies may be more conducive to life than Earth. However, the observability of these `superhabitable' planets in the habitable zones around K dwarf stars has not been fully modeled. This study addresses this gap by modeling the atmospheres of superhabitable exoplanets. We employed the 1D model Atmos to define the superhabitable parameter space, POSEIDON to calculate synthetic transmission spectra, and PandExo to simulate JWST observations. Our results indicate that planets orbiting mid-type K dwarfs, receiving 80% of Earth's solar flux, are optimal for life. These planets sustain temperate surfaces with moderate CO_2 levels, unlike those receiving 60% flux, where necessarily higher CO_2 levels could hinder biosphere development. Moreover, they are easier to observe, requiring significantly fewer transits for biosignature detection compared to Earth-like planets around Sun-like stars. For instance, detecting biosignature pairs like oxygen and methane from 30 parsecs would require 150 transits (43 years) for a superhabitable planet, versus over 1700 transits (sim 1700 years) for Earth-like planets. While such observation times lie outside of JWST mission timescales, our study underscores the necessity of next-generation telescopes and provides valuable targets for future observations with, for example, the ELT.

We present a new regular grid search algorithm for quick fixed-radius nearest-neighbor lookup developed in Python. This module indexes a set of k-dimensional points in a regular grid, with optional periodic conditions, providing a fast approach for nearest neighbors queries. In this first installment we provide three types of queries: bubble, shell and the nth-nearest; as well as three different metrics of interest in astronomy: the euclidean and two distance functions in spherical coordinates of varying precision, haversine and Vincenty; and the possibility of providing a custom distance function. This package results particularly useful for large datasets where a brute-force search turns impractical.

We present a far-infrared all-sky atlas from a sensitive all-sky survey using the Japanese AKARI satellite. The survey covers > 99% of the sky in four photometric bands centred at 65 μm, 90 μm, 140 μm, and 160 μm with spatial resolutions ranging from 1 to 1.5 arcmin. These data provide crucial information for the investigation and characterisation of the properties of dusty material in the Interstellar Medium (ISM), since significant portion of its energy is emitted between sim50 and 200 μm. The large-scale distribution of interstellar clouds, their thermal dust temperatures and column densities, can be investigated with the improved spatial resolution compared to earlier all-sky survey observations. In addition to the point source distribution, the large-scale distribution of ISM cirrus emission, and its filamentary structure, are well traced. We have made the first public release of the full-sky data to provide a legacy data set for use by the astronomical community.

We present the third data release of the European Space Agency's Gaia mission, GDR3. The GDR3 catalogue is the outcome of the processing of raw data collected with the Gaia instruments during the first 34 months of the mission by the Gaia Data Processing and Analysis Consortium. The GDR3 catalogue contains the same source list, celestial positions, proper motions, parallaxes, and broad band photometry in the G, G_{BP}, and G_{RP} pass-bands already present in the Early Third Data Release. GDR3 introduces an impressive wealth of new data products. More than 33 million objects in the ranges G_{rvs} < 14 and 3100 <T_{eff} <14500 , have new determinations of their mean radial velocities based on data collected by Gaia. We provide G_{rvs} magnitudes for most sources with radial velocities, and a line broadening parameter is listed for a subset of these. Mean Gaia spectra are made available to the community. The GDR3 catalogue includes about 1 million mean spectra from the radial velocity spectrometer, and about 220 million low-resolution blue and red prism photometer BPRP mean spectra. The results of the analysis of epoch photometry are provided for some 10 million sources across 24 variability types. GDR3 includes astrophysical parameters and source class probabilities for about 470 million and 1500 million sources, respectively, including stars, galaxies, and quasars. Orbital elements and trend parameters are provided for some 800,000 astrometric, spectroscopic and eclipsing binaries. More than 150,000 Solar System objects, including new discoveries, with preliminary orbital solutions and individual epoch observations are part of this release. Reflectance spectra derived from the epoch BPRP spectral data are published for about 60\,000 asteroids. Finally, an additional data set is provided, namely the Gaia Andromeda Photometric Survey (abridged)

The Gamma-ray detection from an astrophysical object indicates the presence of an extreme environment where high-energy radiation is produced. With the continuous monitoring of the Gamma-ray sky by the Fermi Large Area Telescope (LAT), leading to deeper sensitivity, the high-energy Gamma-ray emission has now been detected from a diverse class of jetted active galactic nuclei (AGN). Here, we present the results of a multiwavelength study of the radio source DA~362, which was reported to be a blazar candidate of uncertain type. However, it was recently identified as a bona fide compact symmetric object (CSO) based on its sub-kpc, bi-polar radio morphology, and lack of radio variability. This makes DA~362 the only fourth Gamma-ray emitting object of this enigmatic class of radio-loud AGN. Using five very long baseline interferometry observations covering 1996-2018, we found the jet separation velocity to be subluminal (v_{rm app}sim 0.2c), thus supporting its CSO nature. Its Fermi-LAT observations revealed a Gamma-ray flaring activity, a phenomenon never detected from the other three Gamma-ray detected CSOs. This object is bright in the near-infrared band but extremely faint in the optical-ultraviolet filters, hinting at possible obscuration. The Swift X-Ray Telescope observation of DA 362 reveals an extremely hard X-ray spectrum, though a strong claim cannot be made due to large uncertainties. We conclude that deeper observations are needed to probe the broadband properties of this enigmatic object and to understand the origin of high-energy Gamma-ray emission.

We present the results of high-resolution (sim1~au) Atacama Large Millimeter/submillimeter Array (ALMA) observations of the TW~Hya protoplanetary disk in the Band 6 dust continuum, as well as the ^13CO and C^18O J=2--1 emission lines. The primary focus of this study is to investigate the kinematics, internal morphology, and local gas environment of the prominent dust blob at a radius of 52~au. By comparing our 2021 data with archival observations from 2017, we detect the proper motion of the blob. The measured azimuthal velocity of 3.3pm0.9~km~s^{-1} is fully consistent with local Keplerian rotation. Combined with the lack of significant radial migration over the four-year baseline, this confirms that the structure is robustly co-moving with the disk system. Crucially, our high-resolution continuum map resolves the blob into a distinct double-peaked morphology separated by sim1.7~au azimuthally. We robustly validate this double-peaked substructure by reproducing it in the independent 2017 dataset using a sparse-modeling image reconstruction technique. We discuss potential physical origins for this double-peaked morphology, including an inclined circumplanetary disk with an inner dust cavity, the roots of planet-induced spiral arms, or alternative hydrodynamic scenarios that do not involve an actively accreting planet such as the U-turn trajectory of secondary dust or a short-lived hydrodynamic gas vortex. We detect no compact gas emission counterparts associated with the continuum blob. Since these CO lines likely trace optically thick upper atmospheric layers, the absence of localized vertical gas perturbations suggests that if an embedded planet is responsible for the dust structure, its mass must be exceptionally low.

Young exoplanets provide a critical link between understanding planet formation and atmospheric evolution. Direct imaging spectroscopy allows us to infer the properties of young, wide orbit, giant planets with high signal-to-noise. This allows us to compare this young population to exoplanets characterized with transmission spectroscopy, which has indirectly revealed the presence of clouds, photochemistry, and a diversity of atmospheric compositions. Direct detections have also been made for brown dwarfs, but direct studies of young giant planets in the mid-infrared were not possible prior to JWST. With two exoplanets around a solar type star, the YSES-1 system is an ideal laboratory for studying this early phase of exoplanet evolution. We report the first direct observations of silicate clouds in the atmosphere of the exoplanet YSES-1 c through its 9-11 micron absorption feature, and the first circumplanetary disk silicate emission around its sibling planet, YSES-1 b. The clouds of YSES-1 c are composed of either amorphous iron-enriched pyroxene or a combination of amorphous MgSiO3 and Mg2SiO4, with particle sizes of less than or equal to 0.1 micron at 1 millibar of pressure. We attribute the emission from the disk around YSES-1 b to be from submicron olivine dust grains, which may have formed through collisions of planet-forming bodies in the disk.

We present the TESS discovery of only the second system of transiting exocomets with a sufficient number of events to measure the size distribution in the RZ Psc system, enabling comparisons with the beta Pictoris and Solar System size distributions. Twenty-four transits with absorption depths (AD) of 1--20\% were observed across three TESS sectors of the 20-50 Myr K0V star, detected as part of our TESS survey of extreme debris disks identified by their IR excess. We discover that the ADs (and hence exocomet radii) follow a broken power-law cumulative frequency distribution not previously seen in extrasolar contexts but similar to that observed in Solar System Kuiper Belt Object sizes, with power-law slopes above and below the break of gamma_AD>break=2.32pm0.12 and gamma_AD<break=0.11pm0.04, respectively. We derive size distributions of 1--7~km from two independent lines of evidence. We use the RZ Psc exocomet rate to predict exocomet yields for the Early eVolution Explorer (EVE) NASA astrophysics Small Explorer (SMEX) mission concept to obtain simultaneous photometry of 10^4 young stars in NUV, optical, and NIR bands. Assuming occurrence rates scaled from RZ Psc, EVE would detect 590 exocomets from approx70 young systems in the optical band, with approx120 simultaneous 5sigma detections in all three bands. These data would enable grain sizes of 200--700~nm and graphite--olivine compositions of dozens of events to be distinguished at 2.5--3sigma, as well as a 4sigma determination of the accuracy of the Herschel-derived M-debris disk fraction.

Object detection, particularly open-vocabulary object detection, plays a crucial role in Earth sciences, such as environmental monitoring, natural disaster assessment, and land-use planning. However, existing open-vocabulary detectors, primarily trained on natural-world images, struggle to generalize to remote sensing images due to a significant data domain gap. Thus, this paper aims to advance the development of open-vocabulary object detection in remote sensing community. To achieve this, we first reformulate the task as Locate Anything on Earth (LAE) with the goal of detecting any novel concepts on Earth. We then developed the LAE-Label Engine which collects, auto-annotates, and unifies up to 10 remote sensing datasets creating the LAE-1M - the first large-scale remote sensing object detection dataset with broad category coverage. Using the LAE-1M, we further propose and train the novel LAE-DINO Model, the first open-vocabulary foundation object detector for the LAE task, featuring Dynamic Vocabulary Construction (DVC) and Visual-Guided Text Prompt Learning (VisGT) modules. DVC dynamically constructs vocabulary for each training batch, while VisGT maps visual features to semantic space, enhancing text features. We comprehensively conduct experiments on established remote sensing benchmark DIOR, DOTAv2.0, as well as our newly introduced 80-class LAE-80C benchmark. Results demonstrate the advantages of the LAE-1M dataset and the effectiveness of the LAE-DINO method.

The near-infrared (NIR) emission of the youngest protostars still needs to be characterized to better understand the evolution of their accretion and ejection activity. We analyze James Webb Space Telescope NIRSpec 1.7 -- 5.3 mum observations of two deeply embedded sources in the S68N protostellar core in Serpens. The North Central (NC) source exhibits a highly obscured spectrum (A_K ~ 4.8 mag) that is modeled with a pre-main-sequence photosphere and a hot disk component. The photospheric parameters are consistent with a young, low-mass photosphere, as suggested by the low surface gravity, log g of 1.95 pm 0.15 cm s^{-2}. The hot disk suggests that accretion onto the central protostellar embryo is ongoing, although prototypical accretion-tracing emission lines HI are not detected. The South Central (SC) source, which is even more embedded (A_K ~ 8 mag; no continuum is detected shortward of 3.6 mum) appears to be driving the large-scale S68N protostellar outflow, and launches a collimated hot molecular jet detected in \Ht and CO ro-vibrational lines. Shock modeling of the \Ht (ro)vibrational lines establishes that fast C-type shocks (geq 30 km s^{-1}), with high pre-shock density (geq 10^7 cm^{-3}), and strong magnetic field (b ~ 3--10, where B = b,times,textrm{n_{H} (cm^{-3})},muG) best match the data. The bright CO fundamental line forest suggests energetic excitation, with the contribution of non-LTE effects, ie irradiation pumping. Detected OH and CH^{+} ro-vibrational lines support this hypothesis. These two Class 0 protostars seem to be in very young evolutionary stages and still have to acquire the bulk of their final stellar masses. These results demonstrate that JWST enables unprecedented diagnostics of these first stages of the protostellar evolutionary phase.

The orbits of planetary systems can be deformed from their initial configurations due to close encounters with larger astrophysical bodies. Typical candidates for close encounters are stars and binaries. We explore the prospect that if there is a sizeable population of primordial black holes (PBH) in our galaxy, then these may also impact the orbits of exoplanets. Specifically, in a simplified setting, we study numerically how many planetary systems might have a close encounter with a PBH, and analyze the potential changes to the orbital parameters of systems that undergo PBH flybys.

The James Webb Space Telescope (JWST) has spectroscopically confirmed galaxies up to zsim14, 300 Myr after the Big Bang, and several candidates have been discovered at zsim15-25, with one candidate as high as zsim30, only 100 Myr after the Big Bang. Such objects are unexpected, since theoretical studies have not predicted the existence of detectable galaxies at zsim30. While any zsim30 candidates may be contaminants at lower redshifts, we explore whether such extreme redshift sources could be consistent with hyper-energetic transient events linked to the formation of the first, metal-free, stars. Specifically, we consider pair-instability supernovae (PISNe), a predicted class of extreme thermonuclear explosions that leave no remnant behind. Using cosmological simulations, we investigate an overdense cosmic region, where star formation and subsequent PISNe occur at zsim30-40, even within standard cosmology. Assessing the likelihood of such a region, the corresponding number of PISNe at zgtrsim20, and their observed flux, we find that JWST has a non-negligible chance to detect a PISN event at extremely high redshifts. If a transient event were confirmed at zsim30, this would provide a direct glimpse into the epoch of first star formation, dramatically extending the empirical reach of astronomy.

Missions to small celestial bodies rely heavily on optical feature tracking for characterization of and relative navigation around the target body. While deep learning has led to great advancements in feature detection and description, training and validating data-driven models for space applications is challenging due to the limited availability of large-scale, annotated datasets. This paper introduces AstroVision, a large-scale dataset comprised of 115,970 densely annotated, real images of 16 different small bodies captured during past and ongoing missions. We leverage AstroVision to develop a set of standardized benchmarks and conduct an exhaustive evaluation of both handcrafted and data-driven feature detection and description methods. Next, we employ AstroVision for end-to-end training of a state-of-the-art, deep feature detection and description network and demonstrate improved performance on multiple benchmarks. The full benchmarking pipeline and the dataset will be made publicly available to facilitate the advancement of computer vision algorithms for space applications.

Every second greater than 10^{25} antineutrinos radiate to space from Earth, shining like a faint antineutrino star. Underground antineutrino detectors have revealed the rapidly decaying fission products inside nuclear reactors, verified the long-lived radioactivity inside our planet, and informed sensitive experiments for probing fundamental physics. Mapping the anisotropic antineutrino flux and energy spectrum advance geoscience by defining the amount and distribution of radioactive power within Earth while critically evaluating competing compositional models of the planet. We present the Antineutrino Global Map 2015 (AGM2015), an experimentally informed model of Earth's surface antineutrino flux over the 0 to 11 MeV energy spectrum, along with an assessment of systematic errors. The open source AGM2015 provides fundamental predictions for experiments, assists in strategic detector placement to determine neutrino mass hierarchy, and aids in identifying undeclared nuclear reactors. We use cosmochemically and seismologically informed models of the radiogenic lithosphere/mantle combined with the estimated antineutrino flux, as measured by KamLAND and Borexino, to determine the Earth's total antineutrino luminosity at 3.4^{+2.3}_{-2.2} times 10^{25} nu_e. We find a dominant flux of geo-neutrinos, predict sub-equal crust and mantle contributions, with sim1% of the total flux from man-made nuclear reactors.

Modern astronomical surveys, such as the Zwicky Transient Facility (ZTF), are capable of detecting thousands of transient events per year, necessitating the use of automated and scalable data analysis techniques. Recent advances in machine learning have enabled the efficient classification and characterization of these transient phenomena. We aim to develop a fully systematic pipeline to identify candidate stellar collision events in galactic nuclei, which may otherwise be identified as tidal disruption events or other transients. We also seek to validate our simulations by comparing key physical parameters derived from observations and used in modeling these events. We generate a comprehensive bank of simulated light curves spanning a range of physical parameters and employ an approximate nearest neighbor algorithm (via the annoy library) to match these with observed ZTF light curves. Our pipeline is successfully able to associate observed ZTF light curves with simulated events. The resulting estimated parameters, including supermassive black hole masses and ejecta mass, are presented and compared to known values when applicable. We demonstrate that a systematic, machine learning-based approach can effectively identify and characterize stellar collision candidate events from large-scale transient surveys. This methodology is especially promising for future surveys which will provide us with significantly high volumes of data, such as LSST, where automated, data-intensive analysis will be critical for advancing our understanding of transient astrophysical phenomena.

Earth remains the only known example of a planet with technology, and future projections of Earth's trajectory provide a basis and motivation for approaching the search for extraterrestrial technospheres. Conventional approaches toward projecting Earth's technosphere include applications of the Kardashev scale, which suggest the possibility that energy-intensive civilizations may expand to harness the entire energy output available to their planet, host star, or even the entire galaxy. In this study, we argue that the Kardashev scale is better understood as a "luminosity limit" that describes the maximum capacity for a civilization to harvest luminous stellar energy across a given spatial domain, and we note that thermodynamic efficiency will always keep a luminosity-limited technosphere from actually reaching this theoretical limit. We suggest the possibility that an advanced technosphere might evolve beyond this luminosity limit to draw its energy directly from harvesting stellar mass, and we also discuss possible trajectories that could exist between Earth today and such hypothetical "stellivores." We develop a framework to describe trajectories for long-lived technospheres that optimize their growth strategies between exploration and exploitation, unlike Earth today. We note that analyses of compact accreting stars could provide ways to test the stellivore hypothesis, and we more broadly suggest an expansion of technosignature search strategies beyond those that reside exactly at the luminosity limit.

A transiting planet invites us to measure its size, mass, orbital parameters, atmospheric composition, and other characteristics. But the invitation can only be accepted if the host star is bright enough for precise measurements of its flux and spectrum. NASA's Transiting Exoplanet Survey Satellite (TESS) is dedicated to finding such favorable systems. Operating from a 13.7-day elliptical orbit around the Earth, TESS uses four 10.5 cm telescopes to capture optical images of a 24 x 96 degree field of view. By shifting the field of view every 27 days, TESS can survey most of the sky every few years. In its first six years, TESS has identified approximately 7,000 planet candidates, with several hundred confirmed as planets. Mass measurements of these planets allow astronomers to differentiate between rocky "super-Earths" and gas-rich or volatile-rich "mini-Neptunes," while observations with the James Webb Space Telescope are revealing the secrets of their atmospheres. TESS has discovered planets orbiting a wide range of stars, including young stars, low-mass stars, binary stars, and even a white dwarf star. Beyond planet detection, TESS probes the optical variability of stars and a diverse array of other astronomical objects, including asteroids, comets, supernovae, and active galactic nuclei.

We present an upgraded version of TRICERATOPS, a software package designed to calculate false positive probabilities for planet candidates identified by the Transiting Exoplanet Survey Satellite (TESS). This enhanced framework now incorporates ground-based light curves in separate bandpasses, which are routinely obtained as part of the candidate vetting process. We apply this upgraded framework to explore the planetary nature of 14 TESS planet candidates, combining primarily J band light curves acquired with the 200-inch Hale Telescope at Palomar Observatory with complementary archival observations from the Las Cumbres Observatory Global Telescope (LCOGT), the Fred Lawrence Whipple Observatory (FLWO), and the Teide Observatory, along with existing TESS data and contrast curves from high-resolution imaging. As a result of this analysis we statistically validate (False Positive Probability < 1.5% and Nearby False Positive Probability < 0.1%) six new planets in five systems: TOI-1346 b, TOI-1346 c, TOI-2719 b, TOI-4155 b, TOI-6000 b, and TOI-6324 b. For these systems, we provide updated estimates of their stellar and planetary properties derived from the TESS and ground-based observations. These new systems contain planets with radii between 0.9-6 Re and orbital periods between 0.3-5.5 days. Finally, we use our upgraded version of TRICERATOPS to quantify the relative importance of multi-wavelength transit photometry and high-resolution imaging for exoplanet candidate validation, and discuss which kinds of candidates typically benefit the most from ground-based multi-color transit observations.

Quasi-nonsingular relative orbital elements (qnsROE) are among the most widely used state representations for formation flying and rendezvous in near-circular inclined orbits. They admit closed-form perturbed state transition matrices (STMs) and a direct geometric mapping, which underpins eccentricity/inclination (E/I)-vector separation for passive safety. Existing eccentric extensions of the qnsROE resolve this geometry in the radial--tangential--normal frame, where a residual semimajor-axis difference makes the E/I separation margin phase dependent. This paper introduces eccentric ROE that reduce to the qnsROE as \(e\to0\) and whose first-order geometry is resolved in the velocity--normal--binormal (VNB) frame, where the secular drift is confined to the velocity direction. Their first-order position and velocity maps yield a closed-form, phase-independent binormal--normal separation bound with an explicit margin for semimajor-axis residuals. Closed-form Keplerian and secular-\(J_2\) STMs and a decoupled impulsive control input matrix are derived for the same elements, and the elements are recovered as the physically normalized integration constants of the Cartesian equations of relative motion in the VNB frame. Together, these results provide a unified ROE framework for formation flying in general elliptic orbits.

The SiTian project, with its vast field of view, will become an ideal platform for asteroid scientific research. In this study, we develop a pipeline to analyze the photometry of asteroids and derive their periods from the data collected by the SiTian pathfinder project Mini-SiTian (MST). The pipeline is applied to the MST f02 region, a MST test region with a sky area of 2.29^{circ} times 1.53^{circ}. Rotation periods of 22 asteroids are derived by the obtained light curves analysis. Among them, there are 8 asteroids available in the Asteroid Lightcurve Photometry Database (ALCDEF), and 6 of them with more photometric points (>200) have similar period parameters as the ones in ALCDEF. Additionally, the periods for 14 of these asteroids are newly obtained and are not listed in ALCDEF. This study demonstrates the feasibility of asteroid photometric research by the SiTian project. It shows that future observations from the SiTian project will provide even more photometry of asteroids, significantly increasing the number of available light curves. The potential vast photometric data of asteroids will help us to further understand the physics of asteroids, their material composition, and the formation and evolution of the solar system.

Recent advances have enabled the discovery of a population of potentially Earth-like planets, yet their orbital eccentricity, which governs their climate and provides clues about their origin and dynamical history, is still largely unconstrained. We identify a sample of 17 transiting exoplanets around late-type stars with similar radii and irradiation to that of Earth and use the "photoeccentric effect" - which exploits transit durations - to infer their eccentricity distribution via hierarchical Bayesian modelling. Our analysis establishes that these worlds further resemble Earth in that their eccentricities are nearly circular (mean eccentricity =0.060_{-0.028}^{+0.040} and leq0.15), with the exception of one outlier of moderate eccentricity. The results hint at a subset population of dynamically warmer Earths, but this requires a larger sample to statistically confirm. The planets in our sample are thus largely subject to minimal eccentricity-induced seasonal variability and are consistent with emerging via smooth disk migration rather than violent planet-planet scattering.

Solar photospheric abundances and CI-chondrite compositions are reviewed and updated to obtain representative solar system abundances of the elements and their isotopes. The new photospheric abundances obtained here lead to higher solar metallicity. Full 3D NLTE photospheric analyses are only available for 11 elements. A quality index for analyses is introduced. For several elements, uncertainties remain large. Protosolar mass fractions are H (X = 0.7060), He (Y = 0.2753), and for metals Li to U (Z = 0.0187). The protosolar (C+N)/H agrees within 13% with the ratio for the solar core from the Borexino experiment. Elemental abundances in CI-chondrites were screened by analytical methods, sample sizes, and evaluated using concentration frequency distributions. Aqueously mobile elements (e.g., alkalis, alkaline earths, etc.) often deviate from normal distributions indicating mobilization and/or sequestration into carbonates, phosphates, and sulfates. Revised CI-chondrite abundances of non-volatile elements are similar to earlier estimates. The moderately volatile elements F and Sb are higher than before, as are C, Br and I, whereas the CI-abundances of Hg and N are now significantly lower. The solar system nuclide distribution curves of s-process elements agree within 4% with s-process predictions of Galactic chemical evolution models. P-process nuclide distributions are assessed. No obvious correlation of CI-chondritic to solar elemental abundance ratios with condensation temperatures is observed, nor is there one for ratios of CI-chondrites/solar wind abundances.

Different theories of dark matter predict different populations and morphologies of galactic subhaloes below about 10^8M_{odot}. The planned angular resolution and optical stability of the Habitable Worlds Observatory (HWO) should enable it to detect subtle subhalo perturbations in strongly lensed galaxy images. We simulate 2000-second exposures with its High Resolution Imager (HRI) under the fiducial Exploratory Analytic Case 1 (EAC1) design, assuming known source structure and omitting lens-galaxy light. Pre-selecting the top 50 of 965 lenses (5.2%) using only arc signal-to-noise and relative gradient power lowers the median mass threshold for 10% aperture coverage roughly tenfold relative to the full ensemble. The top 12 (1.2%) reach a median of 6.6times10^7M_{odot} for the same coverage. At their most sensitive positions, the top 50 and top 12 reach median thresholds of 3times10^7M_{odot} and 1.5times10^7M_{odot}, respectively. Non-linear fits support the Fisher forecasts at the tested masses and positions. For these 12 lenses, increasing the physical wavefront error from 35 to 65 nm RMS raises the median thresholds by only 0.1 dex when the point spread function (PSF) is modelled correctly. Deliberately mismodelling the 35-nm reference PSF removes detectable positions, although false detections remain rare. Under our illustrative retention criteria, loss of sensitive area sets a median tolerated wavefront mismatch of 5 nm RMS (range 2--10 nm). Selective HWO follow-up of lenses discovered by Euclid or Roman could therefore transform our understanding of the nature of dark matter, provided that the PSF is characterized accurately enough to preserve this sensitivity.

With the advent of new spectroscopic surveys from ground and space, observing up to hundreds of millions of galaxies, spectra classification will become overwhelming for standard analysis techniques. To prepare for this challenge, we introduce a family of deep learning tools to classify features in one-dimensional spectra. As the first application of these Galaxy Spectra neural Networks (GaSNets), we focus on tools specialized at identifying emission lines from strongly lensed star-forming galaxies in the eBOSS spectra. We first discuss the training and testing of these networks and define a threshold probability, PL, of 95% for the high quality event detection. Then, using a previous set of spectroscopically selected strong lenses from eBOSS, confirmed with HST, we estimate a completeness of ~80% as the fraction of lenses recovered above the adopted PL. We finally apply the GaSNets to ~1.3M spectra to collect a first list of ~430 new high quality candidates identified with deep learning applied to spectroscopy and visually graded as highly probable real events. A preliminary check against ground-based observations tentatively shows that this sample has a confirmation rate of 38%, in line with previous samples selected with standard (no deep learning) classification tools and follow-up by Hubble Space Telescope. This first test shows that machine learning can be efficiently extended to feature recognition in the wavelength space, which will be crucial for future surveys like 4MOST, DESI, Euclid, and the Chinese Space Station Telescope (CSST).

The spin of the supermassive black hole that resides at the Galactic Centre can in principle be measured by accurate measurements of the orbits of stars that are much closer to SgrA* than S2, the orbit of which recently provided the measurement of the gravitational redshift and the Schwarzschild precession. The GRAVITY near-infrared interferometric instrument combining the four 8m telescopes of the VLT provides a spatial resolution of 2-4 mas, breaking the confusion barrier for adaptive-optics-assisted imaging with a single 8-10m telescope. We used GRAVITY to observe SgrA* over a period of six months in 2019 and employed interferometric reconstruction methods developed in radio astronomy to search for faint objects near SgrA*. This revealed a slowly moving star of magnitude 18.9 in K band within 30mas of SgrA*. The position and proper motion of the star are consistent with the previously known star S62, which is at a substantially larger physical distance, but in projection passes close to SgrA*. Observations in August and September 2019 easily detected S29, with K-magnitude of 16.6, at approximately 130 mas from SgrA*. The planned upgrades of GRAVITY, and further improvements in the calibration, hold the promise of finding stars fainter than magnitude 19 at K.

The Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) is a "collaboration of collaborations" that is using the Canada-France-Hawai'i Telescope, the Pan-STARRS telescopes, and the Subaru Observatory to obtain ugriz images of a core survey region of 6250 deg^2 of the northern sky. The 10sigma point source depth of the data, as measured within a 2-arcsecond diameter aperture, are [u,g,r,i,z] = [23.7, 24.5, 24.2, 23.8, 23.3]\ in AB magnitudes. UNIONS is addressing some of the most fundamental questions in astronomy, including the properties of dark matter, the growth of structure in the Universe from the very smallest galaxies to large-scale structure, and the assembly of the Milky Way. It is set to become the major ground-based legacy survey for the northern hemisphere for the next decade and provides an essential northern complement to the static-sky science of the Vera C. Rubin Observatory's Legacy Survey of Space and Time. UNIONS supports the core science mission of the {\it Euclid} space mission by providing the data necessary in the northern hemisphere for the calibration of the wavelength dependence of the {\it Euclid} point-spread function and derivation of photometric redshifts in the North Galactic Cap. This region contains the highest quality sky for {\it Euclid}, with low backgrounds from the zodiacal light, stellar density, extinction, and emission from Galactic cirrus. Here, we describe the UNIONS survey components, science goals, data products, and the current status of the overall program.

Nanosatellites are proliferating as low-cost dedicated sensing systems with lean development cycles. Kyushu Institute of Technology and collaborators have launched a joint venture for a nanosatellite mission, VERTECS. The primary mission is to elucidate the formation history of stars by observing the optical-wavelength cosmic background radiation. The VERTECS satellite will be equipped with a small-aperture telescope and a high-precision attitude control system to capture the cosmic data for analysis on the ground. However, nanosatellites are limited by their onboard memory resources and downlink speed capabilities. Additionally, due to a limited number of ground stations, the satellite mission will face issues meeting the required data budget for mission success. To alleviate this issue, we propose an on-orbit system to autonomously classify and then compress desirable image data for data downlink prioritization and optimization. The system comprises a prototype Camera Controller Board (CCB) which carries a Raspberry Pi Compute Module 4 which is used for classification and compression. The system uses a lightweight Convolutional Neural Network (CNN) model to classify and determine the desirability of captured image data. The model is designed to be lean and robust to reduce the computational and memory load on the satellite. The model is trained and tested on a novel star field dataset consisting of data captured by the Sloan Digital Sky Survey (SDSS). The dataset is meant to simulate the expected data produced by the 6U satellite. The compression step implements GZip, RICE or HCOMPRESS compression, which are standards for astronomical data. Preliminary testing on the proposed CNN model results in a classification accuracy of about 100\% on the star field dataset, with compression ratios of 3.99, 5.16 and 5.43 for GZip, RICE and HCOMPRESS that were achieved on tested FITS image data.

gPhoton is a new database product and software package that enables analysis of GALEX ultraviolet data at the photon level. The project's stand-alone, pure-Python calibration pipeline reproduces the functionality of the original mission pipeline to reduce raw spacecraft data to lists of time-tagged, sky-projected photons, which are then hosted in a publicly available database by the Mikulski Archive at Space Telescope (MAST). This database contains approximately 130 terabytes of data describing approximately 1.1 trillion sky-projected events with a timestamp resolution of five milliseconds. A handful of Python and command line modules serve as a front-end to interact with the database and to generate calibrated light curves and images from the photon-level data at user-defined temporal and spatial scales. The gPhoton software and source code are in active development and publicly available under a permissive license. We describe the motivation, design, and implementation of the calibration pipeline, database, and tools, with emphasis on divergence from prior work, as well as challenges created by the large data volume. We summarize the astrometric and photometric performance of gPhoton relative to the original mission pipeline. For a brief example of short time domain science capabilities enabled by gPhoton, we show new flares from the known M dwarf flare star CR Draconis. The gPhoton software has permanent object identifiers with the ASCL (ascl:1603.004) and DOI (doi:10.17909/T9CC7G). This paper describes the software as of version v1.27.2.

Our understanding of extra-solar planet systems is highly driven by advances in observations in the past decade. Thanks to high precision spectrograph, we are able to reveal unseen companions to stars with the radial velocity method. High precision photometry from the space, especially with the Kepler mission, enables us to detect planets when they transit their stars and dim the stellar light by merely one percent or smaller. Ultra wide-field, high cadence, continuous monitoring of the Galactic bulge from different sites around the southern hemisphere provides us the opportunity to observe microlensing effects caused by planetary systems from the solar neighborhood, all the way to the Milky Way center. The exquisite AO imaging from ground-based large telescopes, coupled with high-contrast coronagraph, captured the photons directly emitted by planets around other stars. In this article, I present a concise review of the extra-solar planet discoveries, discussing the strengths and weaknesses of the major planetary detection methods, providing an overview of our current understanding of planetary formation and evolution given the tremendous observations delivered by various methods, as well as on-going and planned observation endeavors to provide a clear picture of extra-solar planetary systems.

The origins of the colors of Trans-Neptunian Objects (TNOs) represent a crucial unresolved question, central to understanding the history of our Solar System. Recent observational surveys have revealed correlations between the eccentricity and inclination of TNOs and their colors. This has rekindled the long-standing debate on whether these colors reflect the conditions of TNO formation or their subsequent collisional evolution. In this study, we address this question with 98.7% certainty, using a model-agnostic, data-driven approach based on causal graphs. First, as a sanity check, we demonstrate how our model can replicate the currently accepted paradigms of TNOs' dynamical history, blindly and without any orbital modeling or physics-based assumptions. In fact, our causal model (with no knowledge of the existence of Neptune) predicts the existence of an unknown perturbing body, i.e., Neptune. We then show how this model predicts, with high certainty, that the color of TNOs is the root cause of their inclination distribution, rather than the other way around. This strongly suggests that the colors of TNOs reflect an underlying dynamical property, most likely their formation location. Moreover, our causal model excludes formation scenarios that invoke substantial color modification by subsequent irradiation. We therefore conclude that the colors of TNOs are predominantly primordial.

The Beta Pictoris system is characterized by a dusty debris disk, in addition to the presence of two already known planets. This makes it a particularly interesting case for studying the formation and evolution of planetary systems at a stage where giant planets have already formed, most of the protoplanetary gas has dissipated, and terrestrial planets could emerge. Our goal here is to explore the possibility of additional planets orbiting beyond the outermost known one, beta Pic b. More specifically, we aim to assess whether additional planets in the system could explain the discrepancy between the predicted cutoff of the disk inner cavity at sim28 au with only two planets, and the observed one at sim50 au. We perform an exhaustive dynamical modeling of the debris disk and the carving of its inner edge, by introducing one or two additional planets beyond beta Pic b, coplanar with the disk. Guided by theoretical predictions for the parameter space - mass, semi-major axis, eccentricity - allowed for additional planets, we further carry out a set of N-body simulations, using the symplectic integrator RMVS3. Our simulations indicate that an additional planet with a low eccentricity of 0.05, a mass between 0.15 and 1 M_{Jup}, and a semi-major axis between 30 and 36 au, would be consistent with the observations of an inner debris disk edge at 50 au. We have also explored the hypotheses of a higher eccentricity and the presence of two additional lower mass planets instead of one, which could also account for these observations. While we have found that one or even two additional planets could explain the observed location of the disk inner edge, these hypothetical planets remain in most cases below the current observational limits of high contrast imaging. Future observational campaigns with improved sensitivity will help lowering these limits and perhaps detect that planet.

Planetary surfaces are typically analyzed using high-level semantic concepts in natural language, yet vast orbital image archives remain organized at the pixel level. This mismatch limits scalable, open-ended exploration of planetary surfaces. Here we present MarScope, a planetary-scale vision-language framework enabling natural language-driven, label-free mapping of Martian landforms. MarScope aligns planetary images and text in a shared semantic space, trained on over 200,000 curated image-text pairs. This framework transforms global geomorphic mapping on Mars by replacing pre-defined classifications with flexible semantic retrieval, enabling arbitrary user queries across the entire planet in 5 seconds with F1 scores up to 0.978. Applications further show that it extends beyond morphological classification to facilitate process-oriented analysis and similarity-based geomorphological mapping at a planetary scale. MarScope establishes a new paradigm where natural language serves as a direct interface for scientific discovery over massive geospatial datasets.

We present an incremental version (4FGL-DR4, for Data Release 4) of the fourth Fermi-LAT catalog containing 7194 gamma-ray sources. Based on the first 14 years of science data in the energy range from 50 MeV to 1 TeV, it uses the same analysis methods as the 4FGL-DR3 catalog did for 12 years of data, with only a few improvements. The spectral parameters, spectral energy distributions, light curves and associations are updated for all sources. We add four new extended sources and modify two existing ones. Among the 6658 4FGL-DR3 sources, we delete 14 and change the localization of 10, while 32 are newly associated, eleven associations are changed and three associations are discarded. We add 546 point sources, among which 8 are considered identified and 229 have a plausible counterpart at other wavelengths. Most are just above the detection threshold, and 14 are transient sources below the detection threshold that can affect the light curves of nearby sources.

Finding scientifically interesting phenomena through slow, manual labeling campaigns severely limits our ability to explore the billions of galaxy images produced by telescopes. In this work, we develop a pipeline to create a semantic search engine from completely unlabeled image data. Our method leverages Vision-Language Models (VLMs) to generate descriptions for galaxy images, then contrastively aligns a pre-trained multimodal astronomy foundation model with these embedded descriptions to produce searchable embeddings at scale. We find that current VLMs provide descriptions that are sufficiently informative to train a semantic search model that outperforms direct image similarity search. Our model, AION-Search, achieves state-of-the-art zero-shot performance on finding rare phenomena despite training on randomly selected images with no deliberate curation for rare cases. Furthermore, we introduce a VLM-based re-ranking method that nearly doubles the recall for our most challenging targets in the top-100 results. For the first time, AION-Search enables flexible semantic search scalable to 140 million galaxy images, enabling discovery from previously infeasible searches. More broadly, our work provides an approach for making large, unlabeled scientific image archives semantically searchable, expanding data exploration capabilities in fields from Earth observation to microscopy. The code, data, and app are publicly available at https://github.com/NolanKoblischke/AION-Search

The stars of the Milky Way carry the chemical history of our Galaxy in their atmospheres as they journey through its vast expanse. Like barcodes, we can extract the chemical fingerprints of stars from high-resolution spectroscopy. The fourth data release (DR4) of the Galactic Archaeology with HERMES (GALAH) Survey, based on a decade of observations, provides the chemical abundances of up to 32 elements for 917 588 stars that also have exquisite astrometric data from the Gaia satellite. For the first time, these elements include life-essential nitrogen to complement carbon, and oxygen as well as more measurements of rare-earth elements critical to modern-life electronics, offering unparalleled insights into the chemical composition of the Milky Way. For this release, we use neural networks to simultaneously fit stellar parameters and abundances across the whole wavelength range, leveraging synthetic grids computed with Spectroscopy Made Easy. These grids account for atomic line formation in non-local thermodynamic equilibrium for 14 elements. In a two-iteration process, we first fit stellar labels to all 1 085 520 spectra, then co-add repeated observations and refine these labels using astrometric data from Gaia and 2MASS photometry, improving the accuracy and precision of stellar parameters and abundances. Our validation thoroughly assesses the reliability of spectroscopic measurements and highlights key caveats. GALAH DR4 represents yet another milestone in Galactic archaeology, combining detailed chemical compositions from multiple nucleosynthetic channels with kinematic information and age estimates. The resulting dataset, covering nearly a million stars, opens new avenues for understanding not only the chemical and dynamical history of the Milky Way, but also the broader questions of the origin of elements and the evolution of planets, stars, and galaxies.

The long-term dynamical evolution of asteroid families is governed by the interplay between orbital and rotational evolution driven by thermal forces and collision. We aim to observationally trace the rotational evolution of main-belt asteroid families over Gyr timescales. We analyzed rotational properties of 8739 asteroids with spin period measurements and 3794 asteroids with obliquity determinations across 28 asteroid families spanning ages from 14~Myrs to 3~Gyrs. We introduced a dimensionless timescale that normalizes each asteroid's family age by its classical YORP timescale, enabling direct comparison of rotational states across different evolutionary stages. We examined two key observables: the fraction of slow rotators (periods greater than or equal to 30 hours) and the polarization fraction (the degree to which asteroid spin poles align correctly with their position in the family's V-shape distribution according to the Yarkovsky theory). Evolution of both quantities were fitted to identify characteristic transition timescales. We discovered that the slow-rotator fraction increases steeply with t and saturates at f_{rm slow} simeq 0.25 around a breakpoint t_{rm bp} simeq 20. This implies a stochastic YORP timescale τ_{rm YORP,stoc} simeq 10,τ_{rm YORP} by comparison with rotational evolution models that include tumbling and weakened YORP torques. The polarization fraction reaches a maximum of simeq 0.8 at t simeq 16 and then decays toward the random limit f_{rm pol} rightarrow 0.5 for t gtrsim 20, indicating an increasing dominance of collisional spin reorientation over time. The rotation properties within different asteroid families offer crucial clues to rotation evolution and can serve as a new dimension for age estimation of asteroid families with more data in the LSST era.

Analyzing the planet at scale with satellite imagery and machine learning is a dream that has been constantly hindered by the cost of difficult-to-access highly-representative high-resolution imagery. To remediate this, we introduce here the WorldStrat dataset. The largest and most varied such publicly available dataset, at Airbus SPOT 6/7 satellites' high resolution of up to 1.5 m/pixel, empowered by European Space Agency's Phi-Lab as part of the ESA-funded QueryPlanet project, we curate nearly 10,000 sqkm of unique locations to ensure stratified representation of all types of land-use across the world: from agriculture to ice caps, from forests to multiple urbanization densities. We also enrich those with locations typically under-represented in ML datasets: sites of humanitarian interest, illegal mining sites, and settlements of persons at risk. We temporally-match each high-resolution image with multiple low-resolution images from the freely accessible lower-resolution Sentinel-2 satellites at 10 m/pixel. We accompany this dataset with an open-source Python package to: rebuild or extend the WorldStrat dataset, train and infer baseline algorithms, and learn with abundant tutorials, all compatible with the popular EO-learn toolbox. We hereby hope to foster broad-spectrum applications of ML to satellite imagery, and possibly develop from free public low-resolution Sentinel2 imagery the same power of analysis allowed by costly private high-resolution imagery. We illustrate this specific point by training and releasing several highly compute-efficient baselines on the task of Multi-Frame Super-Resolution. High-resolution Airbus imagery is CC BY-NC, while the labels and Sentinel2 imagery are CC BY, and the source code and pre-trained models under BSD. The dataset is available at https://zenodo.org/record/6810791 and the software package at https://github.com/worldstrat/worldstrat .

The TESS mission produces a large amount of time series data, only a small fraction of which contain detectable exoplanetary transit signals. Deep learning techniques such as neural networks have proved effective at differentiating promising astrophysical eclipsing candidates from other phenomena such as stellar variability and systematic instrumental effects in an efficient, unbiased and sustainable manner. This paper presents a high quality dataset containing light curves from the Primary Mission and 1st Extended Mission full frame images and periodic signals detected via Box Least Squares (Kovács et al. 2002; Hartman 2012). The dataset was curated using a thorough manual review process then used to train a neural network called Astronet-Triage-v2. On our test set, for transiting/eclipsing events we achieve a 99.6% recall (true positives over all data with positive labels) at a precision of 75.7% (true positives over all predicted positives). Since 90% of our training data is from the Primary Mission, we also test our ability to generalize on held-out 1st Extended Mission data. Here, we find an area under the precision-recall curve of 0.965, a 4% improvement over Astronet-Triage (Yu et al. 2019). On the TESS Object of Interest (TOI) Catalog through April 2022, a shortlist of planets and planet candidates, Astronet-Triage-v2 is able to recover 3577 out of 4140 TOIs, while Astronet-Triage only recovers 3349 targets at an equal level of precision. In other words, upgrading to Astronet-Triage-v2 helps save at least 200 planet candidates from being lost. The new model is currently used for planet candidate triage in the Quick-Look Pipeline (Huang et al. 2020a,b; Kunimoto et al. 2021).

Photoionized nebulae comprise basically HII regions and planetary nebulae, and their abundances give important clues on the nucleosynthesis and chemical evolution of their host galaxies. There is presently a large amount of data on these objects, especially for the elements He and N, which are strongly affected by the evolution of intermediate mass stars, as well as O, Ne, S, and Ar, which are essentially synthesized in stars with larger masses. The abundances of these elements in several systems in the Local Group are discussed on the basis of distance-independent correlations.

We use new interior models of cold planets to investigate the mass-radius relationships of solid exoplanets, considering planets made primarily of iron, silicates, water, and carbon compounds. We find that the mass-radius relationships for cold terrestrial-mass planets of all compositions we considered follow a generic functional form that is not a simple power law: log_{10} R_s = k_1 + 1/3 log_{10}(M_s) - k_2 M_s^{k_3} for up to M_p approx 20 M_{oplus}, where M_s and R_s are scaled mass and radius values. This functional form arises because the common building blocks of solid planets all have equations of state that are well approximated by a modified polytrope of the form rho = rho_0 + c P^n. We find that highly detailed planet interior models, including temperature structure and phase changes, are not necessary to derive solid exoplanet bulk composition from mass and radius measurements. For solid exoplanets with no substantial atmosphere we have also found that: with 5% fractional uncertainty in planet mass and radius it is possible to distinguish among planets composed predominantly of iron or silicates or water ice but not more detailed compositions; with sim~5% uncertainty water ice planets with gtrsim 25% water by mass may be identified; the minimum plausible planet size for a given mass is that of a pure iron planet; and carbon planet mass-radius relationships overlap with those of silicate and water planets due to similar zero-pressure densities and equations of state. We propose a definition of "super Earths'' based on the clear distinction in radii between planets with significant gas envelopes and those without.

Giant planets form in protoplanetary discs, where the coupled dynamical and chemical evolution of gas and solids determines the composition of the material they accrete. We investigate how planet formation and migration shape the primordial elemental makeup of giant-planet atmospheres. Our aim is to link atmospheric compositions to planets' formation pathways and the time-dependent chemical properties of their natal discs. We couple 1D models of viscously evolving discs - incorporating radial dust drift and volatile chemistry - with N-body simulations of planetesimals interacting with a growing and migrating giant planet. Four chemical scenarios and three representative grain sizes (0.1, 20, and 100 micron) are explored. We track the accretion of carbon, oxygen, nitrogen, and sulphur to derive atmospheric elemental ratios normalised to stellar values (* denotes stellar normalisation). We identify three atmospheric classes corresponding to distinct accretion regimes: gas-dominated, characterised by N/O* > C/O* > C/N* and unconstrained or substellar S/N* (near-stellar C/S*); planetesimal-dominated, showing N/O* < C/O* < C/N*, S/N* >= C/N*, and C/S* <= C/O*; and drift-enhanced, exhibiting N/O* < C/O* < C/N* and markedly superstellar volatile-to-refractory ratios. N/O*, C/N*, and S/N* vary systematically with migration extent, although degeneracies arise for planets forming beyond the CO and N2 snowlines; C/O* remains largely insensitive. Metallicity alone does not uniquely trace the solid-to-gas accretion balance in drift-dominated regimes. Variations in the disc's chemical state and dust size imprint distinctive volatile-ratio patterns across these classes, providing complementary constraints on disc properties. This multi-element framework establishes predictive trends to guide the interpretation of atmospheric spectra from facilities like JWST and Ariel.

We present an overview of the James Webb Space Telescope (JWST) Advanced Deep Extragalactic Survey (JADES), an ambitious program of infrared imaging and spectroscopy in the GOODS-S and GOODS-N deep fields, designed to study galaxy evolution from high redshift to cosmic noon. JADES uses about 770 hours of Cycle 1 guaranteed time largely from the Near-Infrared Camera (NIRCam) and Near-Infrared Spectrograph (NIRSpec) instrument teams. In GOODS-S, in and around the Hubble Ultra Deep Field and Chandra Deep Field South, JADES produces a deep imaging region of ~45 arcmin^2 with an average of 130 hrs of exposure time spread over 9 NIRCam filters. This is extended at medium depth in GOODS-S and GOODS-N with NIRCam imaging of ~175 arcmin^2 with an average exposure time of 20 hrs spread over 8-10 filters. In both fields, we conduct extensive NIRSpec multi-object spectroscopy, including 2 deep pointings of 55 hrs exposure time, 14 medium pointings of ~12 hrs, and 15 shallower pointings of ~4 hrs, targeting over 5000 HST and JWST-detected faint sources with 5 low, medium, and high-resolution dispersers covering 0.6-5.3 microns. Finally, JADES extends redward via coordinated parallels with the JWST Mid-Infrared Instrument (MIRI), featuring ~9 arcmin^2 with 43 hours of exposure at 7.7 microns and twice that area with 2-6.5 hours of exposure at 12.8 microns For nearly 30 years, the GOODS-S and GOODS-N fields have been developed as the premier deep fields on the sky; JADES is now providing a compelling start on the JWST legacy in these fields.

We estimate the relative likelihood of success in the searches for primitive versus intelligent life on other planets. Taking into account the larger search volume for detectable artificial electromagnetic signals, we conclude that both searches should be performed concurrently, albeit with significantly more funding dedicated to primitive life. Based on the current federal funding allocated to the search for biosignatures, our analysis suggests that the search for extraterrestrial intelligence (SETI) may merit a federal funding level of at least 10$ million per year, assuming that the average lifetime of technological species exceeds a millennium.

With the development of aerospace technology, the increasing population of space debris has posed a great threat to the safety of spacecraft. However, the low intensity of reflected light and high angular velocity of space debris impede the extraction. Besides, due to the limitations of the ground observation methods, small space debris can hardly be detected, making it necessary to enhance the spacecraft's capacity for space situational awareness (SSA). Considering that traditional methods have some defects in low-SNR target detection, such as low effectiveness and large time consumption, this paper proposes a method for low-SNR streak extraction based on local contrast and maximum likelihood estimation (MLE), which can detect space objects with SNR 2.0 efficiently. In the proposed algorithm, local contrast will be applied for crude classifications, which will return connected components as preliminary results, and then MLE will be performed to reconstruct the connected components of targets via orientated growth, further improving the precision. The algorithm has been verified with both simulated streaks and real star tracker images, and the average centroid error of the proposed algorithm is close to the state-of-the-art method like ODCC. At the same time, the algorithm in this paper has significant advantages in efficiency compared with ODCC. In conclusion, the algorithm in this paper is of high speed and precision, which guarantees its promising applications in the extraction of high dynamic targets.

Volatiles like H_2O are present as ice in solids in the outer cold regions of protoplanetary disks and as vapor in the warm inner regions within the water snow line. Icy pebbles drifting inwards from the outer disk sublimate after crossing the snow line, enriching the inner disk with solid mass and water vapor. Meanwhile, proto-planets forming within the disk open gaps in the disk gas, creating traps against the inward drift of pebbles and in turn reducing water enrichment in the inner disk. Recent disk observations from millimeter interferometry and infrared spectroscopy have supported this broad picture by finding a correlation between the outer radial distribution of pebbles and the properties of inner water vapor spectra. In this work, we aim at further informing previous and future observations by building on previous models to explore pebble drift in disks with multiple gaps. We systematically explore multiple gap locations and their depths (equivalent to specific masses of planets forming within), and different particle sizes to study their impact on inner disk water enrichment. We find that the presence of close-in deep gaps carved by a Jupiter-mass planet is likely crucial for blocking icy pebble delivery into the inner disk, while planets with lower masses only provide leaky traps. We also find that disks with multiple gaps show lower vapor enrichment in the inner disk. Altogether, these model results support the idea that inner disk water delivery and planet formation are regulated by the mass and location of the most massive planets.

Capturing a loose object inside a spacecraft requires a robot to approach a target that can translate and rotate, and whose motion changes when contact is made. These conditions motivate evaluation beyond manipulation of supported objects. We introduce AstroBench, an open benchmark for free-floating capture and space robotic manipulation with a fixed-base arm. The task suite combines four initial-motion tracks with sample-storage operations based on MELFI hardware and cap and captive-screw servicing based on the Robotic Refueling Mission. The two workcells provide nine task definitions and 29 task-target combinations. A metadata-filtered Objaverse inventory supplies 8,414 object instances for asset expansion, alongside five cabin visual contexts and versioned task instructions. We report two acquisition-focused experiments using existing checkpoints. A frozen π_{0.5} DROID joint-position policy captures 2 of 200 static targets and 4 of 200 moving targets across twenty object identities. A cube-specific ACT policy captures 8 of 10 static targets and 6 of 10 moving targets in a separate experiment. Each experiment retains its recorded observation, action and capture configuration. The results document difficult direct transfer for the tested generalist checkpoint and successful task-specific capture behavior, while identifying concrete settings for studying motion-aware perception and contact-sensitive control. Code, task definitions and editable workcell assets are available at https://github.com/AstroUpLab/AstroBench.

We present an assessment of the effects of stellar wind magnetic and mechanical components on the habitability of Earth-like exoplanets orbiting the inner and outer radii of the habitable zone (HZ) of M dwarfs. We consider stars with masses in the range of 0.09 - 0.75 M_odot and planets with a surface dipolar magnetic field of 0.5 G. We estimate the size of the magnetospheres of such exoplanets using the pressure balance equation including the contribution of magnetic and ram pressures from stellar winds. We explore different scenarios, including fast and slow stellar winds, to assess the relevance of kinetic contribution. Furthermore, the effect of tidal locking and potential deviations from the Parker spiral, typically used to describe the interplanetary magnetic field, are analyzed. We show that for low mass stars (M < 0.15 M_odot), the ram pressure exerted by stellar winds affects the size of the magnetosphere more than the stellar wind magnetic pressure. Interestingly, when the ram pressure is not much stronger than the magnetic pressure, typically for higher mass stars, the inclusion of ram pressure can be beneficial to the magnetosphere due to the magnetopause currents. A magnetosphere with the size of that of modern Earth is difficult to achieve with the current assumptions. However, an early Earth magnetosphere is achieved by roughly half of our hypothetical planets orbiting the outer radius of the HZ in most of the considered cases. We find that deviations from the Parker spiral can affect the results significantly, reducing the magnetosphere by 56% in extreme cases. Most of the hypothetical planets are most likely (or might be) tidally locked, with the notable exception of those orbiting the outer HZ of GJ 846 and V1005 Ori.

In the era of mega-constellations, the need for accurate and publicly available information has become fundamental for satellite operators to guarantee the safety of spacecrafts and the Low Earth Orbit (LEO) space environment. This study critically evaluates the accuracy and reliability of publicly available ephemeris data for a LEO mega-constellation - Starlink. The goal of this work is twofold: (i) compare and analyze the quality of the data against high-precision numerical propagation. (ii) Leverage Physics-Informed Machine Learning to extract relevant satellite quantities, such as non-conservative forces, during the decay process. By analyzing two months of real orbital data for approximately 1500 Starlink satellites, we identify discrepancies between high precision numerical algorithms and the published ephemerides, recognizing the use of simplified dynamics at fixed thresholds, planned maneuvers, and limitations in uncertainty propagations. Furthermore, we compare data obtained from multiple sources to track and analyze deorbiting satellites over the same period. Empirically, we extract the acceleration profile of satellites during deorbiting and provide insights relating to the effects of non-conservative forces during reentry. For non-deorbiting satellites, the position Root Mean Square Error (RMSE) was approximately 300 m, while for deorbiting satellites it increased to about 600 m. Through this in-depth analysis, we highlight potential limitations in publicly available data for accurate and robust Space Situational Awareness (SSA), and importantly, we propose a data-driven model of satellite decay in mega-constellations.

Recent JWST observations with NIRCam and MIRI of the ultra-short-period super-Earth 55 Cancri e indicate a possible volatile atmosphere surrounding the planet. Previous analysis of the NIRCam spectra suggested potential absorption features from CO2 or CO and significant sub-weekly variability. The MIRI low-resolution spectrum does not contain substantial features but was found to be consistent with effective heat redistribution models. In this work, we computed a grid of over 25000 self-consistent 1D forward models incorporating H-N-O-C-S-P-Si-Ti equilibrium chemistry and assessed plausible atmospheric compositions based on the current JWST data. Despite exhaustive analysis, the composition and properties of the atmosphere remain elusive. While our results statistically favour a global, hydrogen-free, nitrogen-dominated atmosphere enriched in PO and CO2, various alternative compositions, including H2O-,CO-, PH3-, or Si-bearing remain viable explanations. Unconstrained heat redistribution efficiency and absolute NIRCam flux are among the largest sources of uncertainty in our analysis. We also find that the heat redistribution factor and surface pressure are highly degenerate with atmospheric composition, and that these parameters cannot be independently constrained using current JWST observations. Furthermore, we show that the observed variability may arise from dynamic interactions between the atmosphere and an underlying magma ocean, driving rapid shifts in atmospheric chemistry and thermal emission. Our results highlight the importance of using self-consistent forward models when analysing novel JWST spectra with limited signal-to-noise ratios -- such as those of 55 Cancri e -- as it allows for a more comprehensive evaluation of potential atmospheric scenarios while also being less sensitive to subtle spectral differences than retrievals...

Strong solar activity is often accompanied by a variety of radio bursts. These bursts are valuable diagnostics of coronal and heliospheric processes and also have potential applications in space weather monitoring and forecasting. However, space weather applications require low-latency, high-sensitivity radio burst recording and reporting capabilities, which have remained limited. In this work, we present the development of a near-realtime radio burst recording and reporting system using the Owens Valley Radio Observatory Long Wavelength Array. The system directly clips data from a realtime buffer and streams them as a live radio dynamic spectrogram. These spectrograms are then processed by a deep-learning-based burst identification module for type III radio bursts. The identifier is based on a YOLO (You Only Look Once) architecture and is trained on synthetic type III radio bursts generated using a physics-based model to achieve accurate and robust detection. This system enables continuous realtime radio spectrum streaming and automatic reporting of type III radio bursts within approximately 10 seconds of their occurrence.

Space debris presents a critical challenge for the sustainability of future space missions, emphasizing the need for robust and standardized identification methods. However, a comprehensive benchmark for rocket body classification remains absent. This paper addresses this gap by introducing the RoBo6 dataset for rocket body classification based on light curves. The dataset, derived from the Mini Mega Tortora database, includes light curves for six rocket body classes: CZ-3B, Atlas 5 Centaur, Falcon 9, H-2A, Ariane 5, and Delta 4. With 5,676 training and 1,404 test samples, it addresses data inconsistencies using resampling, normalization, and filtering techniques. Several machine learning models were evaluated, including CNN and transformer-based approaches, with Astroconformer reporting the best performance. The dataset establishes a common benchmark for future comparisons and advancements in rocket body classification tasks.

We present results from Atacama Large Millimeter/submillimeter Array (ALMA) spectral line-scan observations at 3-mm and 2-mm bands of three near-infrared-dark (NIR-dark) galaxies behind two massive lensing clusters MACS J0417.5-1154 and RXC J0032.1+1808. Each of these three sources is a faint (de-lensed S_{1.2 mm} < 1 mJy) triply lensed system originally discovered in the ALMA Lensing Cluster Survey. We have successfully detected CO and [C I] emission lines and confirmed that their spectroscopic redshifts are z=3.652, 2.391, and 2.985. By utilizing a rich multi-wavelength data set, we find that the NIR-dark galaxies are located on the star formation main sequence in the intrinsic stellar mass range of log (M_*/M_odot) = 9.8 - 10.4, which is about one order of magnitude lower than that of typical submillimeter galaxies (SMGs). These NIR-dark galaxies show a variety in gas depletion times and spatial extent of dust emission. One of the three is a normal star-forming galaxy with gas depletion time consistent with a scaling relation, and its infrared surface brightness is an order of magnitude smaller than that of typical SMGs. Since this galaxy has an elongated axis ratio of sim 0.17, we argue that normal star-forming galaxies in an edge-on configuration can be heavily dust-obscured. This implies that existing deep WFC3/F160W surveys may miss a fraction of typical star-forming main-sequence galaxies due to their edge-on orientation.

The increasing population of objects in geostationary orbit has raised concerns about the potential risks posed by debris clouds resulting from fragmentation. The short-term evolution and associated hazards of debris generated by collisions in the geostationary region is investigated in this study. The initial distribution of two debris clouds is modeled using a single probability density function.The combined distribution of the evolved clouds is determined by solving boundary value problems.The risks associated with these debris clouds are evaluated by calculating the instantaneous impact rate and cumulative collision probability.The probability of collisions with millimeter-sized fragments may increase to 1% within 36 hours, while the probability of collisions with fragments 5 cm or larger is approximately 10^{-5}.These findings underscore the vulnerability of the geostationary region to space traffic accidents.

Deep Learning (DL) has shown remarkable results in solving inverse problems in various domains. In particular, the Tikhonet approach is very powerful to deconvolve optical astronomical images (Sureau et al. 2020). Yet, this approach only uses the ell_2 loss, which does not guarantee the preservation of physical information (e.g. flux and shape) of the object reconstructed in the image. In Nammour et al. (2021), a new loss function was proposed in the framework of sparse deconvolution, which better preserves the shape of galaxies and reduces the pixel error. In this paper, we extend Tikhonet to take into account this shape constraint, and apply our new DL method, called ShapeNet, to optical and radio-interferometry simulated data set. The originality of the paper relies on i) the shape constraint we use in the neural network framework, ii) the application of deep learning to radio-interferometry image deconvolution for the first time, and iii) the generation of a simulated radio data set that we make available for the community. A range of examples illustrates the results.

The advent of next-generation radio telescopes is set to transform radio astronomy by producing massive data volumes that challenge traditional processing methods. Deep learning techniques have shown strong potential in automating radio analysis tasks, yet are often constrained by the limited availability of large annotated datasets. Recent progress in self-supervised learning has led to foundational radio vision models, but adapting them for new tasks typically requires coding expertise, limiting their accessibility to a broader astronomical community. Text-based AI interfaces offer a promising alternative by enabling task-specific queries and example-driven learning. In this context, Large Language Models (LLMs), with their remarkable zero-shot capabilities, are increasingly used in scientific domains. However, deploying large-scale models remains resource-intensive, and there is a growing demand for AI systems that can reason over both visual and textual data in astronomical analysis. This study explores small-scale Vision-Language Models (VLMs) as AI assistants for radio astronomy, combining LLM capabilities with vision transformers. We fine-tuned the LLaVA VLM on a dataset of 59k radio images from multiple surveys, enriched with 38k image-caption pairs from the literature. The fine-tuned models show clear improvements over base models in radio-specific tasks, achieving ~30% F1-score gains in extended source detection, but they underperform pure vision models and exhibit ~20% drop on general multimodal tasks. Inclusion of caption data and LoRA fine-tuning enhances instruction-following and helps recover ~10% accuracy on standard benchmarks. This work lays the foundation for future advancements in radio VLMs, highlighting their potential and limitations, such as the need for better multimodal alignment, higher-quality datasets, and mitigation of catastrophic forgetting.

We report on analysis of the two-color VR CCD observations of the newly discovered variable 2MASS J18024395+4003309=VSX J180243.9+400331 obtained using the 1-m telescope of the Mt. Lemmon Observatory (LOAO) in the field of the intermediate polar V1323 Her. The extended version of this conference talk we published in 2015JASS...32..127A. The variability was reported in 2012OAP....25..150A, and the object was monitored. The two-color observations covered all phase interval. The object is classified as an Algol-type variable with tidally distorted components, and shows an asymmetry of the maxima (the O\'Connell effect). For phenomenological modeling, we used the trigonometric polynomial approximation of statistically optimal degree, and a recent method "NAV" (New Algol Variable) using local specific shapes for the eclipse. Methodological aspects are described, especially for the case of few color observations. Estimates of the physical parameters based on analysis of phenomenological parameters, are presented.

With its exquisite sensitivity, wavelength coverage, and spatial and spectral resolution, the James Webb Space Telescope is poised to revolutionise our view of the distant, high-redshift (z>5) Universe. While Webb's spectroscopic observations will be transformative for the field, photometric observations play a key role in identifying distant objects and providing more comprehensive samples than accessible to spectroscopy alone. In addition to identifying objects, photometric observations can also be used to infer physical properties and thus be used to constrain galaxy formation models. However, inferred physical properties from broadband photometric observations, particularly in the absence of spectroscopic redshifts, often have large uncertainties. With the development of new tools for forward modelling simulations it is now routinely possible to predict observational quantities, enabling a direct comparison with observations. With this in mind, in this work, we make predictions for the colour evolution of galaxies at z=5-15 using the FLARES: First Light And Reionisation Epoch Simulations cosmological hydrodynamical simulation suite. We predict a complex evolution, driven predominantly by strong nebular line emission passing through individual bands. These predictions are in good agreement with existing constraints from Hubble and Spitzer as well as some of the first results from Webb. We also contrast our predictions with other models in the literature: while the general trends are similar we find key differences, particularly in the strength of features associated with strong nebular line emission. This suggests photometric observations alone should provide useful discriminating power between different models.

The measured neutron flux from the Moons south polar region shows evidence of locally enhanced hydrogen concentrations, likely in the form of water ice, within most permanently shadowed regions (PSR), poleward of 77 deg S latitude. Results are consistent with the original findings of Watson et al, 1961, which found that the PSRs cryogenic surfaces create exclusive conditions for the sequestration of water ice, due to their extremely low sublimation rates. Widespread PSR hydrogenation is demonstrated in several studies by showing that the contrasting PSR area distribution is being instrumentally blurred. The PSRs expected hydrogen observations are correlated by their area fraction of the fixed 30 km diameter footprint area of the Collimated Sensor for Epithermal Neutrons (CSETN), which is part of the Lunar Exploration Neutron Detector (LEND) onboard the Lunar Reconnaissance Orbiter (LRO). The correlation indicates that the PSRs are similarly hydrogenated, with an expected concentration = 0.27 wt%, relative to that of the anhydrous reference terrain (lower bounds). Hydrogen concentrations are demonstrated to be correlated to maximum temperature distributions within the basins of Haworth, Shoemaker and Faustini PSRs. Cabeus-1 PSR shows an anomalously enhanced hydrogen concentration indicating a second process contributes to its hydrogen budget. Results are consistent with ongoing processes that introduce volatiles to the surface including outgassing, solar wind production with regolith silicates, and mixing from small scale meteor impacts and diurnal temperature variation. We validate the bandpass filter used to subtract CSETNs detection of uncollimated neutrons with profiles of several PSRs neutron suppression before and after processing. Keywords: Moon, Epithermal Neutron, Hydrogen, Water, Ice, Volatiles, LRO, LEND, Diviner, LOLA

While machine-learned models are now routinely employed to facilitate astronomical inquiry, model inputs tend to be limited to a primary data source (namely images or time series) and, in the more advanced approaches, some metadata. Yet with the growing use of wide-field, multiplexed observational resources, individual sources of interest often have a broad range of observational modes available. Here we construct an astronomical multimodal dataset and propose AstroM^3, a self-supervised pre-training approach that enables a model to learn from multiple modalities simultaneously. Specifically, we extend the CLIP (Contrastive Language-Image Pretraining) model to a trimodal setting, allowing the integration of time-series photometry data, spectra, and astrophysical metadata. In a fine-tuning supervised setting, our results demonstrate that CLIP pre-training improves classification performance for time-series photometry, where accuracy increases from 84.6% to 91.5%. Furthermore, CLIP boosts classification accuracy by up to 12.6% when the availability of labeled data is limited, showing the effectiveness of leveraging larger corpora of unlabeled data. In addition to fine-tuned classification, we can use the trained model in other downstream tasks that are not explicitly contemplated during the construction of the self-supervised model. In particular we show the efficacy of using the learned embeddings for misclassifications identification, similarity search, and anomaly detection. One surprising highlight is the "rediscovery" of Mira subtypes and two Rotational variable subclasses using manifold learning and dimension reduction algorithm. To our knowledge this is the first construction of an n>2 mode model in astronomy. Extensions to n>3 modes is naturally anticipated with this approach.

We present a machine learning search for local, low-mass galaxies (z < 0.02 and 10^6 M_odot < M_* < 10^9 M_odot) using the combined photometric data from the DESI Imaging Legacy Surveys and the WISE survey. We introduce the spectrally confirmed training sample, discuss evaluation metrics, investigate the features, compare different machine learning algorithms, and find that a 7-class neural network classification model is highly effective in separating the signal (local, low-mass galaxies) from various contaminants, reaching a precision of 95% and a recall of 76%. The principal contaminants are nearby sub-L^* galaxies at 0.02 < z < 0.05 and nearby massive galaxies at 0.05 < z < 0.2. We find that the features encoding surface brightness information are essential to achieving a correct classification. Our final catalog, which we make available, consists of 112,859 local, low-mass galaxy candidates, where 36,408 have high probability (p_{rm signal} > 0.95), covering the entire Legacy Surveys DR9 footprint. Using DESI-EDR public spectra and data from the SAGA and ELVES surveys, we find that our model has a precision of sim 100%, 96%, and 97%, respectively, and a recall of sim 51%, 68% and 53%, respectively. The results of those independent spectral verification demonstrate the effectiveness and efficiency of our machine learning classification model.

Modern astronomical surveys are producing datasets of unprecedented size and richness, increasing the potential for high-impact scientific discovery. This possibility, coupled with the challenge of exploring a large number of sources, has led to the development of novel machine-learning-based anomaly detection approaches, such as Astronomaly. For the first time, we test the scalability of Astronomaly by applying it to almost 4 million images of galaxies from the Dark Energy Camera Legacy Survey. We use a trained deep learning algorithm to learn useful representations of the images and pass these to the anomaly detection algorithm isolation forest, coupled with Astronomaly's active learning method, to discover interesting sources. We find that data selection criteria have a significant impact on the trade-off between finding rare sources such as strong lenses and introducing artefacts into the dataset. We demonstrate that active learning is required to identify the most interesting sources and reduce artefacts, while anomaly detection methods alone are insufficient. Using Astronomaly, we find 1635 anomalies among the top 2000 sources in the dataset after applying active learning, including eight strong gravitational lens candidates, 1609 galaxy merger candidates, and 18 previously unidentified sources exhibiting highly unusual morphology. Our results show that by leveraging the human-machine interface, Astronomaly is able to rapidly identify sources of scientific interest even in large datasets.

Spacecraft collision avoidance procedures have become an essential part of satellite operations. Complex and constantly updated estimates of the collision risk between orbiting objects inform the various operators who can then plan risk mitigation measures. Such measures could be aided by the development of suitable machine learning models predicting, for example, the evolution of the collision risk in time. In an attempt to study this opportunity, the European Space Agency released, in October 2019, a large curated dataset containing information about close approach events, in the form of Conjunction Data Messages (CDMs), collected from 2015 to 2019. This dataset was used in the Spacecraft Collision Avoidance Challenge, a machine learning competition where participants had to build models to predict the final collision risk between orbiting objects. This paper describes the design and results of the competition and discusses the challenges and lessons learned when applying machine learning methods to this problem domain.

We introduce a new large-scale dataset for the advancement of object detection techniques and overhead object detection research. This satellite imagery dataset enables research progress pertaining to four key computer vision frontiers. We utilize a novel process for geospatial category detection and bounding box annotation with three stages of quality control. Our data is collected from WorldView-3 satellites at 0.3m ground sample distance, providing higher resolution imagery than most public satellite imagery datasets. We compare xView to other object detection datasets in both natural and overhead imagery domains and then provide a baseline analysis using the Single Shot MultiBox Detector. xView is one of the largest and most diverse publicly available object-detection datasets to date, with over 1 million objects across 60 classes in over 1,400 km^2 of imagery.

The radii and orbital periods of 4000+ confirmed/candidate exoplanets have been precisely measured by the Kepler mission. The radii show a bimodal distribution, with two peaks corresponding to smaller planets (likely rocky) and larger intermediate-size planets, respectively. While only the masses of the planets orbiting the brightest stars can be determined by ground-based spectroscopic observations, these observations allow calculation of their average densities placing constraints on the bulk compositions and internal structures. Yet an important question about the composition of planets ranging from 2 to 4 Earth radii still remains. They may either have a rocky core enveloped in a H2-He gaseous envelope (gas dwarfs) or contain a significant amount of multi-component, H2O-dominated ices/fluids (water worlds). Planets in the mass range of 10-15 Earth masses, if half-ice and half-rock by mass, have radii of 2.5 Earth radii, which exactly match the second peak of the exoplanet radius bimodal distribution. Any planet in the 2-4 Earth radii range requires a gas envelope of at most a few mass percentage points, regardless of the core composition. To resolve the ambiguity of internal compositions, we use a growth model and conduct Monte Carlo simulations to demonstrate that many intermediate-size planets are water worlds.

Earth Observation (EO) provides critical planetary data for environmental monitoring, disaster management, climate science, and other scientific domains. Here we ask: Are AI systems ready for reliable Earth Observation? We introduce \datasetnamenospace, a benchmark of 140 yes/no questions from NASA Earth Observatory articles across 13 topics and 17 satellite sensors. Using Google Earth Engine API as a tool, LLM agents can only achieve an accuracy of 33% because the code fails to run over 58% of the time. We improve the failure rate for open models by fine-tuning synthetic data, allowing much smaller models (Llama-3.1-8B) to achieve comparable accuracy to much larger ones (e.g., DeepSeek-R1). Taken together, our findings identify significant challenges to be solved before AI agents can automate earth observation, and suggest paths forward. The project page is available at https://iandrover.github.io/UnivEarth.

Euclid is an ESA mission designed to constrain the properties of dark energy and gravity via weak gravitational lensing and galaxy clustering. It will carry out a wide area imaging and spectroscopy survey (EWS) in visible and near-infrared, covering roughly 15,000 square degrees of extragalactic sky on six years. The wide-field telescope and instruments are optimized for pristine PSF and reduced straylight, producing very crisp images. This paper presents the building of the Euclid reference survey: the sequence of pointings of EWS, Deep fields, Auxiliary fields for calibrations, and spacecraft movements followed by Euclid as it operates in a step-and-stare mode from its orbit around the Lagrange point L2. Each EWS pointing has four dithered frames; we simulate the dither pattern at pixel level to analyse the effective coverage. We use up-to-date models for the sky background to define the Euclid region-of-interest (RoI). The building of the reference survey is highly constrained from calibration cadences, spacecraft constraints and background levels; synergies with ground-based coverage are also considered. Via purposely-built software optimized to prioritize best sky areas, produce a compact coverage, and ensure thermal stability, we generate a schedule for the Auxiliary and Deep fields observations and schedule the RoI with EWS transit observations. The resulting reference survey RSD_2021A fulfills all constraints and is a good proxy for the final solution. Its wide survey covers 14,500 square degrees. The limiting AB magnitudes (5σ point-like source) achieved in its footprint are estimated to be 26.2 (visible) and 24.5 (near-infrared); for spectroscopy, the H_α line flux limit is 2times 10^{-16} erg cm^{-2} s^{-1} at 1600 nm; and for diffuse emission the surface brightness limits are 29.8 (visible) and 28.4 (near-infrared) mag arcsec^{-2}.

Machine learning is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using machine learning algorithms for atmospheric retrieval by implementing a random forest to perform retrievals in seconds that are consistent with the traditional, computationally-expensive nested-sampling retrieval method. We expand upon their approach by presenting a new machine learning model, plan-net, based on an ensemble of Bayesian neural networks that yields more accurate inferences than the random forest for the same data set of synthetic transmission spectra. We demonstrate that an ensemble provides greater accuracy and more robust uncertainties than a single model. In addition to being the first to use Bayesian neural networks for atmospheric retrieval, we also introduce a new loss function for Bayesian neural networks that learns correlations between the model outputs. Importantly, we show that designing machine learning models to explicitly incorporate domain-specific knowledge both improves performance and provides additional insight by inferring the covariance of the retrieved atmospheric parameters. We apply plan-net to the Hubble Space Telescope Wide Field Camera 3 transmission spectrum for WASP-12b and retrieve an isothermal temperature and water abundance consistent with the literature. We highlight that our method is flexible and can be expanded to higher-resolution spectra and a larger number of atmospheric parameters.

We describe a novel method for the application of Convolutional Neural Networks (CNNs) to fields defined on the sphere, using the HEALPix tessellation scheme. Specifically, We have developed a pixel-based approach to implement convolutional layers on the spherical surface, similarly to what is commonly done for CNNs in Euclidian space. The algorithm is fully integrable with existing libraries for NNs (e.g., PyTorch or TensorFlow). We present two applications: (i) recognition of handwritten digits projected on the sphere; (ii) estimation of cosmological parameter from Cosmic Microwave Background (CMB) simulated maps. We have built a simple NN architecture, consisting in four convolutional+pooling layers, and have used it for all the applications explored herein. For what concerns the handwritten digits, our CNN reaches an accuracy of about 95%, comparable with other existing spherical CNNs. For CMB applications, we have tested the CNN on the estimation of a "mock" parameter, defining the angular scale at which the power spectrum of a Gaussian field projected on the sphere peaks. We have estimated this parameter directly from maps, in several cases: temperature and polarization, presence of noise and partial sky coverage. In all the cases, the NN performances are comparable with those from standard spectrum-based bayesian methods. We demonstrate, for the first time, the capability of CNNs to extract information from polarization fields and to distinguish between E and B-modes. Lastly, we have applied our CNN to the estimation of the Thomson scattering optical depth at reionization (tau) from simulated CMB maps. Even without any specific optimization of the NN architecture, we reach an accuracy comparable with standard bayesian methods. This work represents a first step towards the exploitation of NNs in CMB parameter estimation and demonstrates the feasibility of our approach.

State-of-the-art space science missions increasingly rely on automation due to spacecraft complexity and the costs of human oversight. The high volume of data, including scientific and telemetry data, makes manual inspection challenging. Machine learning offers significant potential to meet these demands. The Euclid space telescope, in its survey phase since February 2024, exemplifies this shift. Euclid's success depends on accurate monitoring and interpretation of housekeeping telemetry and science-derived data. Thousands of telemetry parameters, monitored as time series, may or may not impact the quality of scientific data. These parameters have complex interdependencies, often due to physical relationships (e.g., proximity of temperature sensors). Optimising science operations requires careful anomaly detection and identification of hidden parameter states. Moreover, understanding the interactions between known anomalies and physical quantities is crucial yet complex, as related parameters may display anomalies with varied timing and intensity. We address these challenges by analysing temperature anomalies in Euclid's telemetry from February to August 2024, focusing on eleven temperature parameters and 35 covariates. We use a predictive XGBoost model to forecast temperatures based on historical values, detecting anomalies as deviations from predictions. A second XGBoost model predicts anomalies from covariates, capturing their relationships to temperature anomalies. We identify the top three anomalies per parameter and analyse their interactions with covariates using SHAP (Shapley Additive Explanations), enabling rapid, automated analysis of complex parameter relationships. Our method demonstrates how machine learning can enhance telemetry monitoring, offering scalable solutions for other missions with similar data challenges.

In an ongoing effort to understand planet formation the link between the chemistry of the protoplanetary disk and the properties of resulting planets have long been a subject of interest. These connections have generally been made between mature planets and young protoplanetary disks through the carbon-to-oxygen (C/O) ratio. In a rare number of systems, young protoplanets have been found within their natal protoplanetary disks. These systems offer a unique opportunity to directly study the delivery of gas from the protoplanetary disk to the planet. In this work we post-process 3D numerical simulations of an embedded Jupiter-massed planet in its protoplanetary disk to explore the chemical evolution of gas as it flows from the disk to the planet. The relevant dust to this chemical evolution is assumed to be small, co-moving grains with a reduced dust-to-gas ratio indicative of the upper atmosphere of a protoplanetary disk. We find that as the gas enters deep into the planet's gravitational well, it warms significantly (up to sim 800 K), releasing all of the volatile content from the ice phase. This change in phase can influence our understanding of the delivery of volatile species to the atmospheres of giant planets. The primary carbon, oxygen, and sulfur carrying ices: CO_2, H_2O, and H_2S are released into the gas phase and along with the warm gas temperatures near the embedded planets lead to the production of unique species like CS, SO, and SO_2 compared to the protoplanetary disk. We compute the column densities of SO, SO_2, CS, and H_2CS in our model and find that their values are consistent with previous observational studies.

We present starkiller, an open-source Python package for forward-modeling flux retrieval from integral field unit spectrograph (IFU) datacubes. Starkiller simultaneously provides stellar spectral classification, relative velocity, and line-of-sight extinction for all sources in a catalog, alongside a source-subtracted datacube. It performs synthetic difference imaging by simulating all catalog sources in the field of view, using the catalog for positions and fluxes to scale stellar models, independent of the datacube. This differencing method is particularly powerful for subtracting both point-sources and trailed or even streaked sources from extended astronomical objects. We demonstrate starkiller's effectiveness in improving observations of extended sources in dense stellar fields for VLT/MUSE observations of comets, asteroids and nebulae. We also show that starkiller can treat satellite-impacted VLT/MUSE observations. The package could be applied to tasks as varied as dust extinction in clusters and stellar variability; the stellar modeling using Gaia fluxes is provided as a standalone function. The techniques can be expanded to imagers and to other IFUs.

We describe the design and commissioning tests for the DSA-110 Not-So-Fast Radio Burst (NSFRB) search pipeline, a 1.4 GHz image-plane single-pulse search sensitive to 134 ms-160.8 s radio bursts. Extending the pulse width range of the Fast Radio Burst (FRB) search by 3 orders of magnitude, the NSFRB search is sensitive to the recently-discovered Galactic Long Period Radio Transients (LPRTs). The NSFRB search operates in real-time, utilizing a custom GPU-accelerated search code, cerberus, implemented in Python with JAX. We summarize successful commissioning sensitivity tests with continuum sources and pulsar B0329+54, estimating the 6sigma flux (fluence) threshold to be ~290 mJy (~40 Jy ms). Future tests of recovery of longer timescale transients, e.g. CHIME J1634+44, are planned to supplement injection testing and B0329+54 observations. An offline DSA-110 NSFRB Galactic Plane Survey was conducted to search for LPRTs, covering -3.5^circ<b<5.7^circ and 141^circ<l<225^circ (~770 square degrees) in Galactic coordinates. We estimate an upper limit Poissonian burst rate ~1 hr^{-1} per square degree (~7 hr^{-1} per 3^circtimes3^circ survey grid cell) maximized across the inner |b|<0.25^circ of the surveyed region. By imposing the ~290 mJy flux limit on two representative models (the magnetar plastic flow model and the White Dwarf-M Dwarf binary model), we reject with 95% confidence the presence of White Dwarf-M Dwarf binary LPRTs with periods between ~10-70s within ~95% of the surveyed region. Combined with the prevalence of LPRTs in the Galactic Plane, our results motivate further consideration of both White Dwarf-M Dwarf binary models and isolated magnetar models. We will continue to explore novel LPRT search strategies during real-time operations, such as triggered periodicity searches and additional targeted surveys.

We present the second Gaia data release, Gaia DR2, consisting of astrometry, photometry, radial velocities, and information on astrophysical parameters and variability, for sources brighter than magnitude 21. In addition epoch astrometry and photometry are provided for a modest sample of minor planets in the solar system. A summary of the contents of Gaia DR2 is presented, accompanied by a discussion on the differences with respect to Gaia DR1 and an overview of the main limitations which are still present in the survey. Recommendations are made on the responsible use of Gaia DR2 results. Gaia DR2 contains celestial positions and the apparent brightness in G for approximately 1.7 billion sources. For 1.3 billion of those sources, parallaxes and proper motions are in addition available. The sample of sources for which variability information is provided is expanded to 0.5 million stars. This data release contains four new elements: broad-band colour information in the form of the apparent brightness in the G_BP (330--680 nm) and G_RP (630--1050 nm) bands is available for 1.4 billion sources; median radial velocities for some 7 million sources are presented; for between 77 and 161 million sources estimates are provided of the stellar effective temperature, extinction, reddening, and radius and luminosity; and for a pre-selected list of 14000 minor planets in the solar system epoch astrometry and photometry are presented. Finally, Gaia DR2 also represents a new materialisation of the celestial reference frame in the optical, the Gaia-CRF2, which is the first optical reference frame based solely on extragalactic sources. There are notable changes in the photometric system and the catalogue source list with respect to Gaia DR1, and we stress the need to consider the two data releases as independent.

Exploring high-latitude lunar regions presents an extremely challenging visual environment for robots. The low sunlight elevation angle and minimal light scattering result in a visual field dominated by a high dynamic range featuring long, dynamic shadows. Reproducing these conditions on Earth requires sophisticated simulators and specialized facilities. We introduce a unique dataset recorded at the LunaLab from the SnT - University of Luxembourg, an indoor test facility designed to replicate the optical characteristics of multiple lunar latitudes. Our dataset includes images, inertial measurements, and wheel odometry data from robots navigating seven distinct trajectories under multiple illumination scenarios, simulating high-latitude lunar conditions from dawn to nighttime with and without the aid of headlights, resulting in 88 distinct sequences containing a total of 1.3M images. Data was captured using a stereo RGB-inertial sensor, a monocular monochrome camera, and for the first time, a novel single-photon avalanche diode (SPAD) camera. We recorded both static and dynamic image sequences, with robots navigating at slow (5 cm/s) and fast (50 cm/s) speeds. All data is calibrated, synchronized, and timestamped, providing a valuable resource for validating perception tasks from vision-based autonomous navigation to scientific imaging for future lunar missions targeting high-latitude regions or those intended for robots operating across perceptually degraded environments. The dataset and all supplementary material can be accessed from and found at https://github.com/spaceuma/spice-hl3.

Virtually all aspects of modern life depend on space technology. Thanks to the great advancement of computer vision in general and deep learning-based techniques in particular, over the decades, the world witnessed the growing use of deep learning in solving problems for space applications, such as self-driving robot, tracers, insect-like robot on cosmos and health monitoring of spacecraft. These are just some prominent examples that has advanced space industry with the help of deep learning. However, the success of deep learning models requires a lot of training data in order to have decent performance, while on the other hand, there are very limited amount of publicly available space datasets for the training of deep learning models. Currently, there is no public datasets for space-based object detection or instance segmentation, partly because manually annotating object segmentation masks is very time consuming as they require pixel-level labelling, not to mention the challenge of obtaining images from space. In this paper, we aim to fill this gap by releasing a dataset for spacecraft detection, instance segmentation and part recognition. The main contribution of this work is the development of the dataset using images of space stations and satellites, with rich annotations including bounding boxes of spacecrafts and masks to the level of object parts, which are obtained with a mixture of automatic processes and manual efforts. We also provide evaluations with state-of-the-art methods in object detection and instance segmentation as a benchmark for the dataset. The link for downloading the proposed dataset can be found on https://github.com/Yurushia1998/SatelliteDataset.

The possible existence of black holes has fascinated scientists at least since Michell and Laplace's proposal that a gravitating object could exist from which light could not escape. In the 20th century, in light of the general theory of relativity, it became apparent that, were such objects to exist, their structure would be far richer than originally imagined. Today, astronomical observations strongly suggest that either black holes, or objects with similar properties, not only exist but may well be abundant in our universe. In light of this, black hole research is now not only motivated by the fascinating theoretical properties such objects must possess but also as an attempt to better understand the universe around us. We review here some selected developments in black hole research, from a review of its early history to current topics in black hole physics research. Black holes have been studied at all levels; classically, semi-classically, and more recently, as an arena to test predictions of candidate theories of quantum gravity. We will review here progress and current research at all these levels as well as discuss some proposed alternatives to black holes.

Next-generation telescopes will bring groundbreaking discoveries but they will also present new technological challenges. The Square Kilometre Array Observatory (SKAO) will be one of the most demanding scientific infrastructures, with a projected data output of 700 PB per year to be distributed to a network of SKA Regional Centres. Current tools are not fully suited to manage such massive data volumes, therefore, new research is required to transform science archives from data providers into service providers. In this paper we examine how a science archive can deliver advanced visualisation capabilities for the SKA science archive. In particular, we have conducted a thorough exploration of existing visualisation software for astronomy and other fields to identify tools capable of addressing Big Data requirements. Using selected technologies, we have developed a prototype archive that provides access to interactive visualisations of 3D radio data through web-based interfaces, adhering to International Virtual Observatory Alliance (IVOA) recommendations to favour interoperability and Open Science practices. In addition, we discuss how current IVOA recommendations support these visualisation capabilities and how they could be expanded. Our prototype archive includes a service to generate 3D models on the fly as a server operation, enabling remote visualisations in a flexible manner; for instance, a set of parameters can be used to customise the models and their visualisation. We have used SKA precursor and pathfinder data to test its usability and scalability, concluding that remote visualisation is a viable solution for handling high-volume data. However, our prototype is constrained by memory limitations, requiring techniques to reduce memory usage.

We use four observations with the European VLBI network to measure the first precise radio parallax of the Crab Pulsar. We found two in-beam extragalactic sources just outside the Crab Nebula, with one bright enough to use as a background reference source in our data. We use the Crab Pulsar's giant pulses to determine fringe and bandpass calibration solutions, which greatly improved the sensitivity and reliability of our images and allowed us to determine precise positional offsets between the pulsar and the background source. From those offsets, we determine a parallax of π=0.53pm0.06;mas and proper motion of (μ_α,μ_δ)=(-11.34pm0.06,2.65pm0.14);mas;yr^{-1}, yielding a distance of d=1.90^{+0.22}_{-0.18};kpc and transverse velocity of v_{perp}=104^{+13}_{-11};km;s^{-1}. These results are consistent with the Gaia 3 measurements, and open up the possibility of far more accurate astrometry with further VLBI observations.

Faint tidal features around galaxies record their merger and interaction histories over cosmic time. Due to their low surface brightnesses and complex morphologies, existing automated methods struggle to detect such features and most work to date has heavily relied on visual inspection. This presents a major obstacle to quantitative study of tidal debris features in large statistical samples, and hence the ability to be able to use these features to advance understanding of the galaxy population as a whole. This paper uses convolutional neural networks (CNNs) with dropout and augmentation to identify galaxies in the CFHTLS-Wide Survey that have faint tidal features. Evaluating the performance of the CNNs against previously-published expert visual classifications, we find that our method achieves high (76%) completeness and low (20%) contamination, and also performs considerably better than other automated methods recently applied in the literature. We argue that CNNs offer a promising approach to effective automatic identification of low surface brightness tidal debris features in and around galaxies. When applied to forthcoming deep wide-field imaging surveys (e.g. LSST, Euclid), CNNs have the potential to provide a several order-of-magnitude increase in the sample size of morphologically-perturbed galaxies and thereby facilitate a much-anticipated revolution in terms of quantitative low surface brightness science.

Understanding the physics of planetary magma oceans has been the subject of growing efforts, in light of the increasing abundance of Solar system samples and extrasolar surveys. A rocky planet harboring such an ocean is likely to interact tidally with its host star, planetary companions, or satellites. To date, however, models of the tidal response and heat generation of magma oceans have been restricted to the framework of weakly viscous solids, ignoring the dynamical fluid behavior of the ocean beyond a critical melt fraction. Here we provide a handy analytical model that accommodates this phase transition, allowing for a physical estimation of the tidal response of lava worlds. We apply the model in two settings: The tidal history of the early Earth-Moon system in the aftermath of the giant impact; and the tidal interplay between short-period exoplanets and their host stars. For the former, we show that the fluid behavior of the Earth's molten surface drives efficient early Lunar recession to {sim} 25 Earth radii within 10^4{-} 10^5 years, in contrast with earlier predictions. For close-in exoplanets, we report on how their molten surfaces significantly change their spin-orbit dynamics, allowing them to evade spin-orbit resonances and accelerating their track towards tidal synchronization from a Gyr to Myr timescale. Moreover, we re-evaluate the energy budgets of detected close-in exoplanets, highlighting how the surface thermodynamics of these planets are likely controlled by enhanced, fluid-driven tidal heating, rather than vigorous insolation, and how this regime change substantially alters predictions for their surface temperatures.

A host of new low-frequency radio telescopes seek to measure the 21-cm transition of neutral hydrogen from the early universe. These telescopes have the potential to directly probe star and galaxy formation at redshifts 20 gtrsim z gtrsim 7, but are limited by the dynamic range they can achieve against foreground sources of low-frequency radio emission. Consequently, there is a growing demand for modern, high-fidelity maps of the sky at frequencies below 200 MHz for use in foreground modeling and removal. We describe a new widefield imaging technique for drift-scanning interferometers, Tikhonov-regularized m-mode analysis imaging. This technique constructs images of the entire sky in a single synthesis imaging step with exact treatment of widefield effects. We describe how the CLEAN algorithm can be adapted to deconvolve maps generated by m-mode analysis imaging. We demonstrate Tikhonov-regularized m-mode analysis imaging using the Owens Valley Long Wavelength Array (OVRO-LWA) by generating 8 new maps of the sky north of δ=-30^circ with 15 arcmin angular resolution, at frequencies evenly spaced between 36.528 MHz and 73.152 MHz, and sim800 mJy/beam thermal noise. These maps are a 10-fold improvement in angular resolution over existing full-sky maps at comparable frequencies, which have angular resolutions ge 2^circ. Each map is constructed exclusively from interferometric observations and does not represent the globally averaged sky brightness. Future improvements will incorporate total power radiometry, improved thermal noise, and improved angular resolution -- due to the planned expansion of the OVRO-LWA to 2.6 km baselines. These maps serve as a first step on the path to the use of more sophisticated foreground filters in 21-cm cosmology incorporating the measured angular and frequency structure of all foreground contaminants.

The origins of the colours of Trans-Neptunian Objects (TNOs) represent a crucial unresolved question, central to understanding the history of our Solar System. Recent observational surveys revealed correlations between the eccentricity and inclination of TNOs, and their colours. This rekindled the long-standing debate on whether these colours reflect the conditions of TNO formation or their subsequent evolution. We address this question using a model-agnostic, data-driven approach that unanimously converges to a common causal graph from the analysis of two different datasets, each from two different conditional independence test methods. For evaluation, we demonstrate how our model is consistent with the currently-accepted paradigms of TNOs' dynamical histories, without involving any orbital modelling or physics-based assumptions. Our causal model (with no knowledge of the existence of Neptune) predicts the need for an unknown confounding variable, consistent with Neptune's effects. The model predicts that the colour of TNOs is the root cause of their inclination distribution, rather than the other way around. This strongly suggests that the colours of TNOs reflect an underlying dynamical property, most likely their formation location. Our model excludes formation scenarios that invoke substantial colour modification by subsequent evolution. We conclude that the colours of TNOs are predominantly primordial.

We investigate the feasibility of a spacecraft mission to conduct a flyby of 3I/ATLAS, the third macroscopic interstellar object discovered on July 1 2025, as it traverses the Solar System. There are both ready-to-launch spacecraft currently in storage on Earth, such as Janus, and spacecraft nearing the end of their missions at Mars. We calculate minimum Delta V single-impulse direct transfer trajectories to 3I/ATLAS both from Earth and from Mars. We consider launch dates spanning January 2025 through March 2026 to explore obtainable and hypothetical mission scenarios. Post-discovery Earth departures require a challenging Delta Vgtrsim24 km s^{-1} to fly by 3I/ATLAS. By contrast, Mars departures from July 2025 - September 2025 require Delta Vsim5 km s^{-1} to achieve an early October flyby -- which is more feasible with existing propulsion capabilities. We discuss how existing spacecraft could be used to observe 3I/ATLAS and how spacecraft at other locations in the Solar System could be repurposed to visit future interstellar objects on short notice.

This paper presents the specification, design, and development of the Visible Camera (VIS) on the ESA Euclid mission. VIS is a large optical-band imager with a field of view of 0.54 deg^2 sampled at 0.1" with an array of 609 Megapixels and spatial resolution of 0.18". It will be used to survey approximately 14,000 deg^2 of extragalactic sky to measure the distortion of galaxies in the redshift range z=0.1-1.5 resulting from weak gravitational lensing, one of the two principal cosmology probes of Euclid. With photometric redshifts, the distribution of dark matter can be mapped in three dimensions, and, from how this has changed with look-back time, the nature of dark energy and theories of gravity can be constrained. The entire VIS focal plane will be transmitted to provide the largest images of the Universe from space to date, reaching m_AB>24.5 with S/N >10 in a single broad I_E~(r+i+z) band over a six year survey. The particularly challenging aspects of the instrument are the control and calibration of observational biases, which lead to stringent performance requirements and calibration regimes. With its combination of spatial resolution, calibration knowledge, depth, and area covering most of the extra-Galactic sky, VIS will also provide a legacy data set for many other fields. This paper discusses the rationale behind the VIS concept and describes the instrument design and development before reporting the pre-launch performance derived from ground calibrations and brief results from the in-orbit commissioning. VIS should reach fainter than m_AB=25 with S/N>10 for galaxies of full-width half-maximum of 0.3" in a 1.3" diameter aperture over the Wide Survey, and m_AB>26.4 for a Deep Survey that will cover more than 50 deg^2. The paper also describes how VIS works with the other Euclid components of survey, telescope, and science data processing to extract the cosmological information.

We report the delivery to the Mikulski Archive for Space Telescopes of target pixel and light curve files for up to 160,000 targets selected from full-frame images (FFI) for each TESS Northern hemisphere observing sector. The data include calibrated target pixels, simple aperture photometry flux time series, and presearch data conditioning corrected flux time series. These data provide TESS users with high quality, uniform pipeline products for a selection of FFI targets, that would otherwise not be readily available. Additionally, we deliver cotrending basis vectors derived from the FFI targets to allow users to perform their own systematic error corrections. The selected targets include all 2-minute targets and additional targets selected from the TESS Input Catalog with a maximum of 10,000 targets per sector on each of the sixteen TESS CCDs. The data products are in the same format as the project-delivered files for the TESS 2-minute targets. All of the TESS-SPOC data products are available at the MAST as a High Level Science Product via https://doi.org/10.17909/t9-wpz1-8s54.

A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy.

We use the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST) to reduce the uncertainty in the local value of the Hubble constant (H_0) from 3.3% to 2.4%. Improvements come from new, near-infrared observations of Cepheid variables in 11 new hosts of recent SNe~Ia, more than doubling the sample of SNe~Ia having a Cepheid-calibrated distance for a total of 19; these leverage the magnitude-z relation based on 300 SNe~Ia at z<0.15. All 19 hosts and the megamaser system NGC4258 were observed with WFC3, thus nullifying cross-instrument zeropoint errors. Other improvements include a 33% reduction in the systematic uncertainty in the maser distance to NGC4258, more Cepheids and a more robust distance to the LMC from late-type DEBs, HST observations of Cepheids in M31, and new HST-based trigonometric parallaxes for Milky Way (MW) Cepheids. We consider four geometric distance calibrations of Cepheids: (i) megamasers in NGC4258, (ii) 8 DEBs in the LMC, (iii) 15 MW Cepheids with parallaxes, and (iv) 2 DEBs in M31. H_0 from each is 72.25+/-2.51, 72.04+/-2.67, 76.18+/-2.37, and 74.50+/-3.27 km/sec/Mpc, respectively. Our best estimate of 73.24+/-1.74 km/sec/Mpc combines the anchors NGC4258, MW, and LMC, and includes systematic errors for a final uncertainty of 2.4%. This value is 3.4 sigma higher than 66.93+/-0.62 km/sec/Mpc predicted by LambdaCDM with 3 neutrinos with mass 0.06 eV and the Planck data, but reduces to 2.1 sigma relative to the prediction of 69.3+/-0.7 km/sec/Mpc with the combination of WMAP+ACT+SPT+BAO, suggesting systematic uncertainties in CMB measurements may play a role in the tension. If we take the conflict between Planck and H_0 at face value, one plausible explanation could involve an additional source of dark radiation in the early Universe in the range of Delta N_eff=0.4-1. We anticipate significant improvements in H_0 from upcoming parallax measurements.

The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with keywords. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), Pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use keywords or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool's versatility through case studies, showcasing its application in various research scenarios. The system's performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, Pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature.

We analyse scattered-light images at multiple epochs of the protoplanetary disc HD139614 taken with GPI and SPHERE instruments. We show that the azimuthal distribution of the surface brightness of the inner bright ring (13-21au) varies with time (likely caused by a structure in the inner-disc at sim1au) and constrain the timescale of this variation to be <11months. We show that inner arc (31-44au) is likely a spiral and tightly wound, measuring a pitch angle of 6.1pm1.2^circ. Additionally, we construct a physical model to explain the broad shadow across sim2/3 of the outer disc and the azimuthally asymmetric bright ring (which has a shadow not aligned with the broad shadow). We model the disc using 1D warp equations with a fast radiative transfer code to produce scattered-light images. We setup the disc as three annuli separated by gaps with an inclined perturbing planet between the inner two annuli. The planet causes the inner annuli to tilt and precess independently casting broad shadows across the outer (un-tilted) annulus. These shadows combine to produce a shadow across sim2/3 of the outer disc and an asymmetric bright ring which matches the observations of HD139614. The precession of the annuli is periodic (so the model matches the observations multiple times) with long precession time-scales (8.4times10^3 and 7.2times10^4years for the inner and middle annulus respectively) so the shadows are effectively static over accessible observational time-scales.

Autonomous space operations such as on-orbit servicing and active debris removal demand robust part-level semantic understanding and precise relative navigation of target spacecraft, yet collecting large-scale real data in orbit remains impractical due to cost and access constraints. Existing synthetic datasets, moreover, suffer from limited target diversity, single-modality sensing, and incomplete ground-truth annotations. We present SpaceSense-Bench, a large-scale multi-modal benchmark for spacecraft perception encompassing 136~satellite models with approximately 70~GB of data. Each frame provides time-synchronized 1024times1024 RGB images, millimeter-precision depth maps, and 256-beam LiDAR point clouds, together with dense 7-class part-level semantic labels at both the pixel and point level as well as accurate 6-DoF pose ground truth. The dataset is generated through a high-fidelity space simulation built in Unreal Engine~5 and a fully automated pipeline covering data acquisition, multi-stage quality control, and conversion to mainstream formats. We benchmark five representative tasks (object detection, 2D semantic segmentation, RGB--LiDAR fusion-based 3D point cloud segmentation, monocular depth estimation, and orientation estimation) and identify two key findings: (i)~perceiving small-scale components (e.g., thrusters and omni-antennas) and generalizing to entirely unseen spacecraft in a zero-shot setting remain critical bottlenecks for current methods, and (ii)~scaling up the number of training satellites yields substantial performance gains on novel targets, underscoring the value of large-scale, diverse datasets for space perception research. The dataset, code, and toolkit are publicly available at https://github.com/wuaodi/SpaceSense-Bench.

We present the discovery of 118 new ultracool dwarf candidates, discovered using a new machine learning tool, named SMDET, applied to time series images from the Wide-field Infrared Survey Explorer. We gathered photometric and astrometric data to estimate each candidate's spectral type, distance, and tangential velocity. This sample has a photometrically estimated spectral class distribution of 28 M dwarfs, 64 L dwarfs, and 18 T dwarfs. We also identify a T subdwarf candidate, two extreme T subdwarf candidates, and two candidate young ultracool dwarfs. Five objects did not have enough photometric data for any estimations to be made. To validate our estimated spectral types, spectra were collected for 2 objects, yielding confirmed spectral types of T5 (estimated T5) and T3 (estimated T4). Demonstrating the effectiveness of machine learning tools as a new large-scale discovery technique.

Satellite Earth-observation (EO) time series in the optical and microwave ranges of the electromagnetic spectrum are often irregular due to orbital patterns and cloud obstruction. Compositing addresses these issues but loses information with respect to vegetation phenology, which is critical for many downstream tasks. Instead, we present TESSERA, a pixel-wise foundation model for multi-modal (Sentinel-1/2) EO time series that learns robust, label-efficient embeddings. During model training, TESSERA uses Barlow Twins and sparse random temporal sampling to enforce invariance to the selection of valid observations. We employ two key regularizers: global shuffling to decorrelate spatial neighborhoods and mix-based regulation to improve invariance under extreme sparsity. We find that for diverse classification, segmentation, and regression tasks, TESSERA embeddings deliver state-of-the-art accuracy with high label efficiency, often requiring only a small task head and minimal computation. To democratize access, adhere to FAIR - principles, and simplify use, we release global, annual, 10m, pixel-wise int8 embeddings together with open weights/code and lightweight adaptation heads, thus providing practical tooling for large-scale retrieval and inference at planetary scale. All code and data are available at: https://github.com/ucam-eo/tessera.

Astronauts take thousands of photos of Earth per day from the International Space Station, which, once localized on Earth's surface, are used for a multitude of tasks, ranging from climate change research to disaster management. The localization process, which has been performed manually for decades, has recently been approached through image retrieval solutions: given an astronaut photo, find its most similar match among a large database of geo-tagged satellite images, in a task called Astronaut Photography Localization (APL). Yet, existing APL approaches are trained only using satellite images, without taking advantage of the millions open-source astronaut photos. In this work we present the first APL pipeline capable of leveraging astronaut photos for training. We first produce full localization information for 300,000 manually weakly labeled astronaut photos through an automated pipeline, and then use these images to train a model, called AstroLoc. AstroLoc learns a robust representation of Earth's surface features through two losses: astronaut photos paired with their matching satellite counterparts in a pairwise loss, and a second loss on clusters of satellite imagery weighted by their relevance to astronaut photography via unsupervised mining. We find that AstroLoc achieves a staggering 35% average improvement in recall@1 over previous SOTA, pushing the limits of existing datasets with a recall@100 consistently over 99%. Finally, we note that AstroLoc, without any fine-tuning, provides excellent results for related tasks like the lost-in-space satellite problem and historical space imagery localization.