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--- |
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license: apache-2.0 |
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datasets: |
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- samfatnassi/gaia-dr3 |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: transformers |
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pipeline_tag: feature-extraction |
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doi: 10.5281/zenodo.18684894 |
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--- |
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# SADIM-54M: Stellar Intelligence Framework |
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**SADIM-54M** 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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### 1. Technical Feature Map |
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The model is designed to process 13 fundamental astronomical parameters: |
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| Feature Name | Description | |
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| :--- | :--- | |
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| **source_id** | Unique Gaia DR3 Identifier | |
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| **ra / dec** | Celestial Equatorial Coordinates | |
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| **l / b** | Galactic Coordinates (Disk Alignment) | |
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| **pmra / pmdec** | Kinematics (Proper Motion Velocity) | |
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| **d_pc** | Distance in Parsecs (1/parallax) | |
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| **x, y, z** | 3D Heliocentric Cartesian Mapping | |
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| **abs_m** | Absolute Magnitude (Intrinsic Brightness) | |
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| **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-54M Model:** For fast inference, predicting missing stellar properties, or automating the classification of new astronomical data. |
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* **Use the Gaia-DR3 Dataset:** For deep-dive research, historical record querying, or training your own custom neural networks. |
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* **Dataset Link:** [samfatnassi/gaia-dr3](https://huggingface.co/datasets/samfatnassi/gaia-dr3) |
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### 3. Quick Start (Python) |
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Since the dataset contains over **1 Billion records**, we recommend using **Streaming Mode**: |
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```python |
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from datasets import load_dataset |
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from transformers import AutoModel |
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# 1. Access the Data |
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dataset = load_dataset("samfatnassi/gaia-dr3", split="train", streaming=True) |
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# 2. Load the Model |
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model = AutoModel.from_pretrained("KilmaAI/SADIM-54M") |
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# Fetch a sample star |
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sample_star = next(iter(dataset)) |
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print(f"Analyzing Star ID: {sample_star['source_id']}") |
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