---
license: apache-2.0
datasets:
- samfatnassi/gaia-dr3
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: feature-extraction
doi: 10.5281/zenodo.18684894
---

# SADIM-54M: Stellar Intelligence Framework

**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.

**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.

### 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-54M 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-54M")

# Fetch a sample star
sample_star = next(iter(dataset))
print(f"Analyzing Star ID: {sample_star['source_id']}")