| --- |
| license: apache-2.0 |
| datasets: |
| - samfatnassi/gaia-dr3 |
| language: |
| - en |
| metrics: |
| - accuracy |
| library_name: transformers |
| pipeline_tag: feature-extraction |
| doi: 10.5281/zenodo.18727667 |
|
|
| --- |
| |
| # SADIM: Stellar Intelligence Framework |
|
|
| **SADIM-77M** is a specialized AI model trained on the **Gaia Data Release 3 (DR3)** catalog. It bridges the gap between massive raw astronomical observations and actionable scientific insights. |
|
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| **Research Objectives** |
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| • Galactic Archaeology: Identifying stellar streams and ancient structures. |
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| • Big Data Optimization: Providing an AI-ready interface for 1B+ records. |
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| • Scalability: Real-time stellar analysis for future space surveys. |
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| **Research Link:** https://zenodo.org/records/18727667 |
|
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| ### 1. Technical Feature Map |
| The model is designed to process 13 fundamental astronomical parameters: |
|
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| | Feature Name | Description | |
| | :--- | :--- | |
| | **source_id** | Unique Gaia DR3 Identifier | |
| | **ra / dec** | Celestial Equatorial Coordinates | |
| | **l / b** | Galactic Coordinates (Disk Alignment) | |
| | **pmra / pmdec** | Kinematics (Proper Motion Velocity) | |
| | **d_pc** | Distance in Parsecs (1/parallax) | |
| | **x, y, z** | 3D Heliocentric Cartesian Mapping | |
| | **abs_m** | Absolute Magnitude (Intrinsic Brightness) | |
| | **bp_rp** | Color Index (Temperature Indicator) | |
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| ### 2. When to use the Model vs. the Dataset? |
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| * **Use the SADIM 77 Model:** For fast inference, predicting missing stellar properties, or automating the classification of new astronomical data. |
| * **Use the Gaia-DR3 Dataset:** For deep-dive research, historical record querying, or training your own custom neural networks. |
| * **Dataset Link:** [samfatnassi/gaia-dr3](https://huggingface.co/datasets/samfatnassi/gaia-dr3) |
|
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| ### 3. Quick Start (Python) |
| Since the dataset contains over **1 Billion records**, we recommend using **Streaming Mode**: |
|
|
| ```python |
| from datasets import load_dataset |
| from transformers import AutoModel |
| |
| # 1. Access the Data |
| dataset = load_dataset("samfatnassi/gaia-dr3", split="train", streaming=True) |
| |
| # 2. Load the Model |
| model = AutoModel.from_pretrained("KilmaAI/SADIM-77M") |
| |
| # Fetch a sample star |
| sample_star = next(iter(dataset)) |
| print(f"Analyzing Star ID: {sample_star['source_id']}") |
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