In summary, we have found quantitative evidence of thermal conduction suppression in a hot flare loop by coronal seismology of the slow-mode waves. This result suggests that the flare loop should cool much slower than expected from the classical Spitzer conductive cooling. Our studied event is indeed of such long-duration events (LDEs) which are the flares with a slower-than-expected decay rate in soft X-ray and EUV radiation (e.g., Forbes & Acton 1996; Takahashi & Watanabe 2000; Qiu et al. 2012). To explain the LDEs and flare loop-top sources, some previous studies have suggested the mechanism of continuous heating (Warren 2006; Liu et al. 2013; Sun et al. 2013) or conduction suppression (McTiernan et al. 1993; Jiang et al. 2006; Li et al. 2012). Our study confirms the effect of the latter mechanism in a more direct way. Laboratory experiments and numerical studies showed that the actual conductivity is smaller (by at least a factor of two) than that given by Spitzer when l ≲ 30λ where l is the temperature gradient scale length and λ the mean free path of thermal electrons (Bell et al. 1981; Luciani et al. 1983). We estimate l/λ ∼ 30 in this case, suggesting that the nonlocal conduction may account for the observed conduction suppression (Matte & Virmont 1982; Rosner et al. 1986). By studying the evolution of flare loop-top sources, Jiang et al. (2006) suggested that plasma waves or turbulence may play an important role in suppressing the conduction during the decay phase of flares. The mechanism is similar to that used for interpreting the significant reduction of thermal conductivity in galaxy-cluster cooling flows by a tangled magnetic field (Chandran & Cowley 1998). Finally, we conclude that the result of conduction suppression may also shed light on the coronal heating problem (see the review by Klimchuk 2015), because weak thermal conductivity implies smaller conductive losses, and an extended lifetime of individual nanoflares, increasing the average coronal temperature for the same heating rate.


Following the approach described in Risaliti & Lusso (2015), there exists a relation between the luminosities in the X-rays (LX) and UV band (LUV)
(8)$$\begin{eqnarray*}
\log (L_\mathrm{ X})=\hat{\gamma }\log (L_{\mathrm{ UV}})+\beta ^{\prime },
\end{eqnarray*}$$where $\hat{\gamma }$ and β′ denote the slope parameter and the intercept. Combining equation (8) with the well-known expression of $L=F\times 4\pi D_L^2$, the luminosity distance can be rewritten as
(9)$$\begin{eqnarray*}
\log (D_L)=\frac{1}{2-2\hat{\gamma }}\times [\hat{\gamma }\log (F_{\mathrm{ UV}}) - \log (F_\mathrm{ X}) + \hat{\beta }],
\end{eqnarray*}$$a function of the respective fluxes (F), the slope parameter ($\hat{\gamma }$), and the normalization constant ($\hat{\beta }=\beta ^{\prime }+(\hat{\gamma }-1)\log _{10} 4\pi$). Therefore, from theoretical point of view, the luminosity distance can be directly determined from the measurements of the fluxes of FX and FUV, with a reliable knowledge of the dispersion δ in this relation and the value for the two parameters ($\hat{\gamma }$, $\hat{\beta }$) characterizing the LX–LUV relation. However, it has been established that the LX–LUV relation was characterized by a high dispersion. Through the analysis of different quasar samples with multiple observations available, previous works derived a consistent value for the slope parameter ($\hat{\gamma }=0.599\pm 0.027$) and the intrinsic dispersion of the relation (0.35–0.40 dex) (Lusso et al. 2010; Young, Risaliti & Elvis 2010). It was found in subsequent analysis quantifying the observational effects (Lusso & Risaliti 2016) that the magnitude of the intrinsic dispersion can be eventually decreased to the level of 0.15 dex. They identified a subsample of quasars without the major contributions from uncertainties in the measurement of the (2 keV) X-ray flux, absorption in the spectrum in the UV and in the X-ray wavelength ranges, variability of the source and non-simultaneity of the observation in the UV and X-ray bands, inclination effects affecting the intrinsic emission of the accretion disc, and the selection effects due to the Eddington bias (Risaliti & Lusso 2019). Besides the lower dispersion in the relation, the reliability and effectiveness of the method strongly depend on the lack of evolution of the relation with redshift (Bisogni et al. 2018). Finally, Risaliti & Lusso (2019) produced a final, high-quality catalogue of 1598 quasars, by applying several filters (X-ray absorption, dust-reddening effects, observational contaminants in the UV, and Eddington bias) to the parent sample from the Sloan Digital Sky Survey (SDSS) quasar catalogues (Shen et al. 2011; Paris et al. 2017) and the XMM–Newton Serendipitous Source Catalogue (Rosen et al. 2016). The final results indicated that such refined selection of the sources could effectively mitigate the large dispersion in the LX–LUV relation, with a tractable amount of scatter avoiding possible contaminants and unknown systematics (see Risaliti & Lusso 2019 for more details). More importantly, the similar analysis has supported the non-evolution of LX–LUV relation with the redshift, which is supported by the subsequent study involving the intercept parameter $\hat{\beta } = 8.24\pm 0.01$, the slope parameter $\hat{\gamma }=0.633\pm 0.002$, and smaller dispersion $\hat{\delta }=0.24$ in a new, larger quasar sample (Risaliti & Lusso 2019).


At large distances, the proper motions of the halo stars are either unreliable or generally unavailable, which hinders a direct measurement of their velocity dispersions. However, our off-centric location in the Galaxy means that the galactocentric radial (r) and heliocentric radial (s) directions are not the same. This difference is more significant in the inner halo, at a distance of r ≲ a couple of times of R0, where R0 is the distance of the Sun from the Galactic Centre. Hence, in the inner halo the observed line-of-sight velocities of the stars can be expressed in terms of all three orthogonal galactocentric velocities (vr, vθ, vϕ), or in other words the line-of-sight velocities have some contribution from the tangential galactocentric velocities. Provided we have a model that well represents the distribution of the halo stars, we can fit a model marginalized over the unknown tangential motions to the available four-dimensional data (position vector and a line-of-sight velocity), and thus estimate the velocity moments of the system. In the absence of proper motion, the approach of estimating moments of the velocities has been extensively used to predict the kinematics of the MW halo. For example, Sirko et al. (2004), Kafle et al. (2012), Kafle et al. (2013), Kafle et al. (2014) and King et al. (2015) fit an ellipsoidal distribution of velocities and similarly, Deason, Belokurov & Evans (2011a) apply an alternative power-law model to derive the halo kinematics. Using the marginalisation scheme, Kafle et al. (2012, 2013) studied halo Blue Horizontal Branch stars (BHBs), Kafle et al. (2014) studied both BHBs and K-Giant stars (KGs), while King et al. (2015) analysed a mixed bag of BHB and F-type stars to cumulatively construct the velocity dispersion and anisotropy of the outer halo. Interestingly, Kafle et al. (2012) found that the velocity anisotropy parameter of the Galactic halo is non-monotonic and has a prominent dip at a galactocentric radius of r ≃ 18 kpc. In their studies, King et al. (2015) find that the value of β at 15 ≲ R/kpc ≲ 25 is more tangentially biased, which they attribute to the difference in the spatial resolutions of the data sets and adopt a marginalization technique. A varying level of undulations in the anisotropy parameter has also been observed in simulated haloes (Rashkov et al. 2013; Loebman et al. 2017). There are a number of proposed scenarios that could explain such a feature, e.g. a transition from inner to outer halo or a local shell-like structure at the given radius. Moreover, it can also be due to the unrelaxed stars dispersed from the kinematically coherent satellite galaxies that are aligned with kinematically coherent planar structures; assuming that such planar structures have strong rotation as suggested by Ibata et al. (2013), Pawlowski, McGaugh & Jerjen (2015), Libeskind et al. (2015) and Ibata et al. (2015), etc. Recently, Loebman et al. (2017) suggest that a major merger as early as redshift z ∼ 1 can also result in a tangential dip in the value of β over a wide range of radii. While Bird & Flynn (2015) suggest that such a feature in the velocity anisotropy run of the halo is a transitory phase, Loebman et al. (2017) conclude that such dips are long-lived in the in situ stellar halo. In any case, there is currently no consensus view as to what causes such velocity anisotropy changes. Finally, in the outer halo there have been recent attempts to utilize multi-epoch Hubble Space Telescope data to estimate the halo velocity dispersion. In particular, recently Cunningham et al. (2016) used the Galactic foreground stars along the M31 galaxy and found that the halo is isotropic at r/kpc ∼ 25. In Fig. 10, we summarize the recent (this paper inclusive) measurements of the halo velocity anisotropy.


The linear relationship that we observe between breakpoint energy and core temperature is in line with previous measurements (e.g. McComas et al. 1992; Štverák et al. 2009), for both the halo and strahl. According to Scudder & Olbert (1979), a linear trend in the halo relation also follows under the assumption that binary Coulomb collisions dominate electron dynamics in the solar wind. However, in order to align with available experimental data, Scudder & Olbert (1979) set a scaling factor of Ebp/kBTc = 7, which differs from our scaling factor of Ebp/kBTc = 5.5 ± 0.1. With a scaling factor of Ebp/kBTc = 7, Scudder & Olbert (1979) predict that a transformation of thermal electrons into the suprathermal population occurs as the solar wind flows out from the Sun. Findings by Štverák et al. (2009), on the other hand, show that the (nh + ns)/nc ratio remains roughly constant with heliocentric distance in the slow wind, suggesting a lack of interchange between the thermal and suprathermal populations. However Štverák et al. (2009) observes some variability in the (nh + ns)/nc ratio in the fast wind, which they attribute to either statistical effects due to a lack of samples or a possible “interplay” between thermal and suprathermal electrons. Scudder & Olbert (1979) also predict that the halo Ebp/kBTc ratio remains constant with heliocentric distance, whereas Štverák et al. (2009) find that the halo Ebp/kBTc ratio decreases with heliocentric distance. These findings by Štverák et al. (2009), along with the discrepancy between our calculated ratio of Ebp/kBTc = 5.5 ± 0.1 and the prediction of Ebp/kBTc = 7, suggest that the model of Scudder & Olbert (1979) requires a minor update to either the theory or to the input parameters. The discrepancy, however, may also be indicative of other processes, such as wave-particle scattering (e.g. Gary et al. 1994), that possibly modifies the ratio between breakpoint energy and core temperature while preserving its linear relationship.


The inventory of solid-state material sometimes combines ice feature investigation with radiative transfer codes to simulate the observed spectral energy distribution and/or chemical models (e.g. Pontoppidan et al. 2005; Ballering et al. 2021). The ice band spectroscopic profiles observed at medium to high spectral resolution intrinsically contain information that can be used to constrain the extent of grain growth since they are affected by this growth. Carbon dioxide displays several characteristics that are particularly interesting for probing grain growth. It is one of the main and ubiquitous ice mantle constituents, along with water and, depending on the line of sight, carbon monoxide. The carbon dioxide stretching mode around 4.27 µm possesses a fairly narrow absorption band with a typical full width at half maximum (FWHM) of tens of cm−1, depending on the exact ice mixture environment (e.g. Ehrenfreund et al. 1996, 1999), whereas the water ice FWHM is several hundred cm−1. In addition to the relatively high contrast expected in the CO2 ice profile due its narrowness, carbon dioxide absorbs in a relatively clean region of the infrared spectrum. For the absorption band of water ice centred at 3.1 the red wing of the profile is modified not only by grain growth but also by additional absorption from, for example, methanol and the 3.47 µm band assigned to the presence of ammonia in the water mantle. The carbon monoxide stretching mode, lying at slightly higher wavelengths than that of carbon dioxide, is also in a relatively clean region. Some sources show a significant absorption at 4.62 µm, attributed to the presence of OCN−, that can mainly affect the blue side of the CO absorption profile. It has been investigated and discussed in Dartois (2006), where it was shown that grain growth to micron sizes can still produce an observed large red component in its absorption profiles towards some lines of sight. Some young stellar object (YSO) spectra can also harbour hydrogen lines in emission, such as Pfund β (4.654 µm) and Brackett α (4.051 µm), which has to be taken into account. Their contribution can either be estimated from the set of observed hydrogen lines and/or taken out of the profile analysis if significant for spectra with high enough spectral resolution given their narrow profile widths. The integrated absorption cross-section of the carbon dioxide band is relatively high, higher than carbon monoxide, an additional factor that makes it a good target for investigating how grain growth affects spectroscopic band profiles.


Finally, our models assume the absence of planetary magnetic fields. The early paradigm considering the evolution of terrestrial planets has implied that the planetary magnetic field is necessary to protect planetary atmospheres and reduce the atmospheric mass loss (see e.g. Dehant et al. 2007, and references therein). The later studies, however, show that this point of view is ambiguous. Thus, the effect of the magnetic field on the atmospheric escape can be considered as a result of the two concurring processes: reducing the escape by capturing the ionised atmospheric species within the closed magnetic field lines, and enhancing the escape of the atmospheric ions through the regions of the open magnetic lines (polar cusps, in the case of a dipole field) and the reconnection on the night-side (see, e.g. Khodachenko et al. 2015; Sakai et al. 2018; Carolan et al. 2021). Thus, for planets in the Solar System, it was shown both in the observations (Gunell et al. 2018; Ramstad & Barabash 2021) and by modelling (Sakai et al. 2018; Egan et al. 2019) that the presence of a weak magnetic field can intensify atmospheric escape. These results, however, should be taken with caution for young planets, and in particular those in the sub-Neptune range, because of the different atmospheric structures and the non-thermal mechanisms dominating the atmospheric mass loss in the Solar System, which are contrary to the planets considered in this study (see, e.g. Scherf & Lammer 2021, for the discussion). For hot Jupiters, Khodachenko et al. (2015) predict a significant suppression of escape for intrinsic magnetic fields larger than 0.3 G. The model with the closest setup to this study by Carolan et al. (2021) predicts, however, for the 0.7Mjup planet experiencing XUV (thermally) driven atmospheric escape, a small increase in the atmospheric mass-loss rate with increasing dipole field strength (about twice between 0 and 5 G). We therefore expect that the possible effect from the planetary intrinsic magnetic field depends largely on the strength and configuration of the planetary and stellar magnetic fields, but, according to the numbers reported in the literature, might not affect our results dramatically. The lack of studies for close-in sub-Neptune-like planets, however, holds us from making final conclusions.


The calculation of mass outflow rates, especially in the ionized gas phase, have often come from measurements using integrated fibre or long-slit spectra, where several assumptions are invoked in the outflow modelling. These assumptions, briefly described here, result in ‘time-averaged global mass outflow rate’ with large systematic uncertainties. First, due to the limitations of the current instruments even on large telescopes, an accurate modelling of the outflow geometry is not possible. This is especially true for high redshift galaxies (z∼2) where, with currently available adaptive optics (AO) technology, one can at best achieve a spatial resolution of ∼2 kpc where the bulk of the outflow might reside (e.g. Brusa et al. 2016; Davies et al. 2020b). Therefore, the outflow geometry is either assumed to be a uniformly filled conical, bi-conical, or spherical thin shells (e.g. Veilleux, Shopbell & Miller 2001; Fischer et al. 2013; Riffel, Storchi-Bergmann & Winge 2013; Ishibashi & Fabian 2015; Thompson et al. 2015; Bae & Woo 2016; Husemann et al. 2019; Mingozzi et al. 2019). Secondly, if the data are obtained from fibre and single-slit spectroscopy, the size of the outflow is largely unconstrained. For long-slit observations, as an example, the outflow size depends on whether the slit is oriented along the outflow direction. This can be mitigated by using integral field spectroscopy (IFS) which is being increasingly used for extragalactic studies (e.g. Liu et al. 2013; Rupke & Veilleux 2013; Harrison et al. 2014; Maiolino et al. 2017; Husemann et al. 2019; Schönell et al. 2019; Rupke, Thomas & Dopita 2021), although there could still be projection effects with the IFS data. Thirdly, accurate determination of electron density and electron temperature is required for the ionized mass outflow rate calculations. Electron density is usually derived from emission lines that arise out of two closely spaced ‘metastable’ energy levels such as [S ii]λλ6716,6731 ([S ii] doublet hereafter). Density measured from the [S ii] doublet is sensitive to values between ∼10 and 5000 cm−3, typical in the Narrow Line Region (NLR) of AGN host galaxies (e.g. Osterbrock & Ferland 2006; Perna et al. 2017; Baron & Netzer 2019; Davies et al. 2020a). The [S ii] doublet is significantly weaker than the lines used to trace ionized outflows such as the [O iii]λ5007 and H α. In high redshift galaxies, it is extremely challenging to detect these doublet lines, despite hours of observations on a single target. Therefore, nominal density values are often assumed in mass outflow rate calculations, resulting in systematic uncertainties of up to 2–3 orders of magnitude. Furthermore, the density structure within the outflowing medium is often non-uniform, when resolved in low redshift galaxies (e.g. Kakkad et al. 2018). Therefore assuming a constant density within the outflowing medium often leads to inaccurate outflow rate and kinetic energy values. Collectively, these assumptions result in a systematic uncertainty of approximately 3–4 orders of magnitude (e.g. Harrison et al. 2018). This implies that the quoted values of coupling efficiency in the literature have a wide range, with the actual efficiency still an unknown in most of the studies.


The number of known Local Group galaxies continues to rise in the era of deep, panchromatic, wide-field surveys such as the SDSS (York et al. 2000), Pan-STARRS (Kaiser et al. 2010), the Dark Energy Survey (DES; Diehl et al. 2014), and PAndAS (McConnachie et al. 2009). A complete catalog of Local Group galaxies discovered up to 2012 was compiled by McConnachie (2012);26

26
An updated list is maintained online at http://www.astro.uvic.ca/alan/Nearby_Dwarf_Database.html
.

 since then, four new M31 satellites have been discovered: Andromeda XXXI, Andromeda XXXII, and Andromeda XXXIII from Pan-STARRS data (Martin et al. 2013b, 2013a), and Andromeda XXX from PAndAS data (Conn et al. 2012; M. J. Irwin et al. 2016, in preparation). More dramatically, 23 new “ultrafaint” satellites have since been discovered in the immediate vicinity of the Milky Way. Four of these detections were made using Pan-STARRS data (Laevens et al. 2015a, 2015b; Martin et al. 2015) while the remaining objects were discovered in the first and second years of the DES (Bechtol et al. 2015; Drlica-Wagner et al. 2015; Kim & Jerjen 2015; Kim et al. 2015a, 2015b; Koposov et al. 2015). For our purposes—where we seek a homogeneous comparison of the Local Group and Virgo luminosity functions, based on NGVS data—we may limit the analysis to Local Group satellites brighter than Mg = −9.13 mag, the 50% detection limit that applies to the Virgo sample. This reduces the Local Group sample to the galaxies listed in Table 5, divided according to whether they are M31 satellites (20), Galactic satellites (8), or members of the Local Group but not associated with either galaxy specifically (10), following the prescription of McConnachie (2012).27

27
A virial radius of 300 kpc is assumed for both the Milky Way and M31. The zero-velocity radius of the Local Group is taken to be 1060 kpc.
 For comparison with Virgo, we assume a color term g − V = 0.14 mag to convert the V-band magnitudes in Table 5 to g-band magnitudes, although, as we will show, the slope is not sensitive to the exact value used for the conversion (or, equivalently, to the value of

).


Various types of AGNs are known and one among them are the narrow-line Seyfert 1 (NLSy1) galaxies, which are classified based on the presence of narrow Hβ emission line with full width at half-maximum (FWHM) 2000 km s−1 and weak [O iii] emission line, with F([O iii])/F(Hβ) 3 (Osterbrock & Pogge 1985; Goodrich 1989). They are believed to be powered by low-mass black holes (${\sim } 10^7 \, \mathrm{M}_{\odot }$) having higher accretion rate and generally showing strong Fe ii emission compared to their broad line counterparts namely the broad-line Seyfert 1 (BLSy1) galaxies (Xu et al. 2012; Rakshit et al. 2017a). However, from spectro-polarimetric observations of a γ-ray emitting NLSy1 galaxy, PKS 2004 − 447 (Baldi et al. 2016) and accretion disc modelling of a sample of 23 radio-detected NLSy1 galaxies (Calderone et al. 2013) indicate that they have masses similar to the blazar class of AGNs. Other characteristics that make NLSy1 galaxies different from the BLSy1 galaxies are their rapid soft X-ray variability (Pounds, Done & Osborne 1995; Leighly 1999a), steep soft X-ray spectra (Boller, Brandt & Fink 1996; Wang, Brinkmann & Bergeron 1996; Leighly 1999b) and low amplitude optical variability (Grupe 2004; Rakshit & Stalin 2017). Also, the fraction of NLSy1 galaxies detected in radio is much lower (${\sim } 7{{\ \rm per\ cent}}$) compared to the fraction of radio detected BLSy1 galaxies (Komossa et al. 2006; Rakshit et al. 2017a). Among radio-loud NLSy1 galaxies, about a dozen (∼2 per cent) have been detected in γ-ray by the Fermi-Large Area Telescope (e.g. Abdo et al. 2009; D’Ammando et al. 2015; Paliya et al. 2018) suggesting the unambiguous presence of relativistic jets in them. Multiband broad-band SED modelling of these γ-ray detected NLSy1 galaxies indicate that these sources have many properties similar to the blazar class of AGNs (Paliya et al. 2013b) and specifically resembling the flat spectrum radio quasar (FSRQ) category (Paliya et al. 2018). In the radio, these γ-ray emitting NLSy1 galaxies have a compact core jet morphology, high brightness temperature, show superluminal motion and significant radio variability (Doi et al. 2006; Komossa et al. 2006). Detailed investigations of the population of NLSy1 galaxies need to be undertaken to understand more about their peculiar characteristics.


The most interesting interpretation of the clumps in and around the Sparkler is that the bulk of them are evolved (maximally old, given the 4.6 Gyr age of universe at the epoch of observation) globular clusters. This is also consistent with Sameie et al. (2022), who find that globular cluster formation in lower-mass galaxies occurs at redshifts of ∼5–10. If this interpretation is correct, JWST observations of quiescent, evolved globular clusters around z ∼ 1.5 galaxies can be used to explore the formation history of globular clusters in a manner that is complementary to searching directly for the earliest stages of globular cluster formation (e.g., by examining young massive star formation complexes at z ∼ 6 and higher; Boylan-Kolchin 2017, 2018). Young star formation complexes may, or may not, evolve eventually into globular clusters, but there can be little doubt about the identity of an isolated and quiescent compact system if its mass is around 106
M
⊙ and its scale length is a few parsecs. JWST observations of evolved globular clusters at z ∼ 1.5 are also complementary to exploring the ages of local globular clusters, as models fit to local globular clusters cannot distinguish between old and very old systems. For example, distinguishing between an ∼11.5 Gyr old stellar population that formed at z = 3 and a 13.2 Gyr old stellar population that formed at z = 9 is not possible with current models and data, because they are degenerate with respect to a number of physical parameters (Ocvirk et al. 2006; Conroy et al. 2009, 2010). JWST observations of evolved globular clusters, seen when the universe was about one third of its present age, provide an opportunity for progress by “meeting in the middle”, because population synthesis models of integrated starlight from SSPs can distinguish rather easily between the ages of young-intermediate stellar populations. This is because intermediate-mass stars with very distinctive photospheric properties are present at these ages. At z = 1.378, the lookback time to the Sparkler is 9.1 Gyr, and the age of the universe at that epoch is 4.6 Gyr. Distinguishing between z = 3 and z = 9 formation epochs for the globular cluster system corresponds to distinguishing between 2.4 Gyr and 4.1 Gyr old populations, which is relatively straightforward for population synthesis models in the JWST bands. In the case of the Sparkler, the striking conclusion is that at least foir of its globular clusters have likely formed at z > 9.


In this section we describe the method used to retrieve the various physical parameters associated with each LIR − z bin of the master catalogue. For all templates, the total LIR is derived directly integrating the templates between 8 and 1000 μm. In order to derive the other main physical parameters such as stellar mass and colour excess for all simulated galaxies, we applied the Multi-wavelength Analysis of Galaxy Physical Properties (MAGPHYS, da Cunha et al. 2008) package, or the software SED3FIT (Berta et al. 2013) based on MAGPHYS when an AGN contribution is present to the set of templates associated with each galaxy population. This SED fitting procedure serves to infer the physical properties associated with each SED template because this information is not available a priori because of the empirical nature of the templates described in Sect. 2.1.1. To perform the fit, we created a large set of simulated observations of custom filters spanning from FUV to far-IR wavelengths and assigning S/N of 10 to each of them. For the AGN component, we considered a library with smooth (Fritz et al. 2006; Feltre et al. 2012) and clumpy tori (Nenkova et al. 2008a,b), as explained in more detail in the next section. The analysis of the AGN component is necessary to disentangle the contribution of the AGN and star formation to the total IR-luminosity and to take into account the contamination of the AGN when deriving the stellar mass. In the fits by both MAGPHYS and SED3FIT we considered Bruzual & Charlot (2003) stellar templates with Chabrier (2003) IMF. For all considered empirical templates we retrieved the best fit value of the different physical parameters estimated by SED3FIT or MAGPHYS, as well as the 2.5%, 16%, 84%,  and 97.5% percentile when available3. When assigning a physical property to a simulated galaxy, we randomised each parameter value following the corresponding probability distribution to take into account the uncertainties on the SED fitting procedure. When a probability distribution is not available, we assumed a Gaussian distribution centred to the best value and an arbitrary σ = 0.3 dex. In the master catalogue we considered only the median value of each parameter, as derived for each galaxy population and LIR − z bin.


An extensive set of non-linear convective pulsation models for RR Lyrae at different metal and helium abundances has been taken into account. The transformation of bolometric magnitude variations into the Gaia filters allowed us to derive the first theoretical light curves directly comparable with Gaia time-series data. In particular, we built the first theoretical Bailey diagrams and PW relations in the GBP, G, and GRP filters, varying both the metallicity and the helium content. As for the Bailey diagram, we conclude that an increase in the metal abundance and/or in the helium abundance produces a decrease in the pulsation amplitudes, whereas an increase in the luminosity level produces a period shift towards longer values. In particular, in the case of FO RR Lyrae, the location of the described bell-shape in the Bailey diagram can be used to constrain the luminosity level (see e.g. Bono et al. 1997). The theoretical PW relations in the Gaia bands show a dependence of the zero-point on metal abundance, in the sense that brighter Wesenheit functions are predicted for more metal poor chemical composition and a lower effect due to variations in the helium content, with helium-enriched models characterized by longer periods and brighter Wesenheit functions. The theoretical PWZ relations are applied to a subset of Gaia DR2 RR Lyrae (293 F and 50 FO pulsators) with complementary metallicity information to infer individual theoretical parallaxes, which are in very good agreement with Gaia results. In particular, the inferred zero-point parallax offset is consistent with zero both in the case of F and FO pulsators. Even if more stringent conclusions could be drawn in the future from the next Gaia data releases, the obtained results seem on one side to support the predictive capabilities of current pulsation models and on the other to suggest that a smaller parallax offset could be required for the bluer older and lower mass RR Lyrae stars than for classical Cepheids.


NGC 2110. NGC 2110 is another nearby (z = 0.00779, Gallimore et al. 1999), X-ray bright Seyfert galaxy. Diniz et al. (2015) report a black hole mass of

2
.

7

−
2.1

+
3.5

×

10
8

M
⊙

$ 2.7^{+ 3.5}_{- 2.1} \times 10^{8}\,{{M}_{\odot}} $

, from the relation with the stellar velocity dispersion. From BeppoSAX data, Malaguti et al. (1999) found the X-ray spectrum to be affected by complex absorption. This has been later confirmed by Evans et al. (2007), who find the Chandra+XMM–Newton data to be well fitted with a neutral, three-zone, partial-covering absorber. Rivers et al. (2014) find the Suzaku data to be well fitted with a stable full-covering absorber plus a variable partial-covering absorber. A soft excess below 1.5 keV is also present (Evans et al. 2007), and possibly due to extended circumnuclear emission seen with Chandra (Evans et al. 2006). No Compton reflection hump has been detected with Suzaku (Rivers et al. 2014) or NuSTAR (Marinucci et al. 2015), despite the presence of a complex Fe Kα line. According to the multi-epoch analysis of Marinucci et al. (2015), the Fe Kα line is likely the sum of a constant component (from distant, Compton-thick material) and a variable one (from Compton-thin material). Concerning the high-energy cut-off, ambiguous results have been reported in literature (see Table 1). Ricci et al. (2017) report a value of

448

−
55

+
63

$ 448^{+63}_{-55} $

 keV, while Lubiński et al. (2016) report a coronal temperature of

230

−
57

+
51

$ 230^{+51}_{-57} $

 keV and an optical depth of

0
.

52

−
0.13

+
0.14

$ 0.52^{+ 0.14}_{- 0.13} $

. From 2008–2009 INTEGRAL data, Beckmann & Do Cao (2010) report a cut-off of ∼80 keV with a hard photon index, but these results are not confirmed by NuSTAR (Marinucci et al. 2015). Indeed, only lower limits to the high-energy cut-off have been found with NuSTAR (210 keV: Marinucci et al. 2015), Suzaku (250 keV: Rivers et al. 2014) and BeppoSAX (143 keV: Risaliti 2002). No hard X-ray spectral variability has been detected by Caballero-Garcia et al. (2012) and Soldi et al. (2014) from BAT data, despite the significant flux variability.