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# stars_types_with_best4_predictions.fits - Description
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## Overview
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This catalog contains effective temperature predictions for 2.1 million eclipsing binary stars.
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It merges the base stars_types.dat catalog with ML predictions using a "best-of-four" ensemble
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approach that selects the prediction with the lowest uncertainty for each object.
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**Creation Date**: 2025-12-18
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**Total Objects**: 2,145,310
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**Objects with Teff**: 2,085,712 (97.2%)
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**Temperature Range**: 2737 - 38456 K
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## Temperature Sources
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1. **Gaia GSP-Phot**: 1,251,127 objects (58.3%)
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- High-quality spectrophotometric temperatures from Gaia DR3
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- No uncertainty estimates provided
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2. **ML Predictions**: 834,585 objects (38.9%)
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- Best-of-four ensemble (selects lowest uncertainty)
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- Four models compared:
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* teff_only: Gaia photometry only (g, BP, RP, BP-RP)
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* teff_logg: Gaia photometry + log(g) with uncertainty propagation
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* teff_cluster: Gaia photometry + cluster probabilities
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* teff_flag1: Gaia photometry (trained on flag 1 high-quality sources)
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- Mean uncertainty: 203 K
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3. **No Prediction**: 59,598 objects (2.8%)
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- Objects without Gaia Teff and outside ML training domain
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## Quality Flags
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Quality assessment based on temperature source and uncertainty:
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- **A**: Gaia GSP-Phot temperature (highest quality) - 1,251,127 objects (58.3%)
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- **B**: ML prediction with uncertainty < 300 K (high confidence) - 736,974 objects (34.4%)
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- **C**: ML prediction with uncertainty < 500 K (medium confidence) - 64,031 objects (3.0%)
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- **D**: ML prediction with uncertainty >= 500 K (low confidence) - 33,580 objects (1.6%)
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- **X**: No temperature available - 59,598 objects (2.8%)
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## Model Selection Distribution (ML predictions only)
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Best-of-four ensemble selects the model with lowest uncertainty for each object:
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- **teff_cluster**: 160,931 objects (19.3%)
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- **teff_flag1**: 243,801 objects (29.2%)
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- **teff_logg**: 149,829 objects (18.0%)
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- **teff_only**: 280,024 objects (33.6%)
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## Uncertainty Statistics (ML predictions only)
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- **Mean**: 203 K
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- **Median**: 168 K
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- **Std Dev**: 144 K
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- **Min**: 17 K
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- **Max**: 5372 K
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- **25th percentile**: 124 K
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- **75th percentile**: 239 K
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## Column Descriptions
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| Column | Type | Unit | Description |
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|--------|------|------|-------------|
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| source_id | int64 | - | Gaia DR3 source identifier |
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| ra | float64 | deg | Right Ascension (J2000) |
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| dec | float64 | deg | Declination (J2000) |
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| period | float64 | d | Orbital period |
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| teff_gaia | float64 | K | Effective temperature from Gaia GSP-Phot (null if unavailable) |
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| binary_type | str | - | Binary type (D=detached, C=overcontact) |
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| amplitude | float64 | mag | Light curve amplitude |
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| teff_predicted | float64 | K | ML predicted temperature from best-of-four (null if no prediction) |
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| teff_uncertainty | float64 | K | ML prediction uncertainty (null for Gaia temperatures) |
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| teff_final | float64 | K | Final temperature (Gaia if available, else ML, null if neither) |
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| teff_source | str | - | Temperature source (Gaia, teff_only, teff_logg, teff_cluster, teff_flag1, none) |
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| quality_flag | str | - | Quality flag (A/B/C/D/X, see above) |
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## Usage Examples
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### Python (Astropy)
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```python
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from astropy.table import Table
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# Load catalog
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catalog = Table.read('stars_types_with_best4_predictions.fits')
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# Filter by quality
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high_quality = catalog[catalog['quality_flag'] <= 'B'] # Gaia or low-uncertainty ML
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print(f"High-quality objects: {len(high_quality):,}")
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# Access temperatures
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teff = catalog['teff_final']
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uncertainty = catalog['teff_uncertainty']
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# Filter by temperature range
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cool_stars = catalog[(catalog['teff_final'] > 3000) & (catalog['teff_final'] < 5000)]
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```
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### Python (Polars)
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