File size: 2,227 Bytes
5a7dd68 2186865 5a7dd68 2186865 5a7dd68 2186865 5a7dd68 2186865 5a7dd68 2186865 5a7dd68 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
---
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.
**Research Objectives**
• Galactic Archaeology: Identifying stellar streams and ancient structures.
• Big Data Optimization: Providing an AI-ready interface for 1B+ records.
• Scalability: Real-time stellar analysis for future space surveys.
**Research Link:** https://zenodo.org/records/18727667
### 1. Technical Feature Map
The model is designed to process 13 fundamental astronomical parameters:
| 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) |
### 2. When to use the Model vs. the Dataset?
* **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)
### 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']}")
|