AstroClusterModel is a machine learning pipeline that clusters astronomical FITS images based on structural and compositional features like planetary blobs, ring structures, and radial intensity profiles. It combines feature engineering, dimensionality reduction (UMAP/PCA), and clustering (K-Means) to uncover meaningful groupings in space imagery.
- 🔍 Extracts 52 handcrafted features: radial, blob, and ring features.
- 🔻 Reduces dimensions using UMAP/PCA.
- 📌 Clusters using K-Means.
- 🧠 Provides cluster interpretations and sample images.
| Feature Group | Description |
|---|---|
| Radial | Mean, std, peaks, zero-crossings in radial intensity profiles |
| Blob | Count, average size, and intensity of blobs (planet-like regions) |
| Ring | Count, radius, and concentricity of rings via Hough Circle Transform |
| Technique | Purpose |
|---|---|
| UMAP | Non-linear reduction for better visual cluster separation |
| PCA | Linear reduction for easier interpretation |
- Uses K-Means on reduced features.
- Automatically interprets clusters using statistical summaries.
- Displays sample images from each cluster.
- Automatically categorize thousands of astronomical images without manual inspection
- Identify exoplanetary systems with similar structural patterns for comparative research
- Flag statistical outliers that may represent new astronomical phenomena or instrument errors
- Provide quantitative metrics for comparing morphological features across star systems