Dedulek/gaia-eb-teff-datasets

Gaia Eclipsing Binary Effective Temperature Datasets

Dataset Description

This dataset contains multi-survey photometry and stellar parameters for 2.18 million eclipsing binary stars from the Gaia mission. It combines data from Gaia DR3, Pan-STARRS DR1, and 2MASS to enable machine learning prediction of effective temperatures (Teff) for stars lacking spectroscopic measurements.

Dataset Summary

• Total objects: 2,179,680 eclipsing binary stars
• Gaia DR3 coverage: 100% (all sources have Gaia photometry)
• Pan-STARRS coverage: 53.5% (1,166,000 sources)
• 2MASS coverage: Variable (J, H, K bands)
• Teff coverage: 58% have Gaia GSP-Phot temperatures
• ML predictions: 38.9% (847,000 stars) have ML-predicted temperatures

Surveys Included

1. Gaia DR3 (2023)

   • G, BP, RP magnitudes and colors
   • GSP-Phot effective temperatures
   • Astrometric parameters

2. Pan-STARRS DR1 (2016)

   • g, r, i, z, y optical magnitudes
   • PSF and Kron photometry

3. 2MASS (2003)

   • J, H, K near-infrared magnitudes

Dataset Structure

Unified Photometry Dataset

File: photometry/eb_unified_photometry.parquet Size: 227 MB Format: Apache Parquet

This is the primary dataset containing all photometry and stellar parameters.

Key Columns

Identifiers:

• source_id (int64): Gaia DR3 source identifier

Gaia Photometry:

• g, bp, rp (float64): Gaia G, BP, RP magnitudes
• bp_rp, g_bp, g_rp (float64): Gaia colors
• parallax, pmra, pmdec (float64): Astrometry

Gaia Stellar Parameters:

• teff_gaia (float64): GSP-Phot effective temperature [K]
• logg_gaia (float64): Surface gravity [log cm/s²]
• mh_gaia (float64): Metallicity [Fe/H]

Pan-STARRS Photometry:

• ps_gPSFMag, ps_rPSFMag, ps_iPSFMag, ps_zPSFMag, ps_yPSFMag (float64): PSF magnitudes
• ps_gKronMag, ps_rKronMag, etc. (float64): Kron magnitudes
• Pan-STARRS colors: ps_g_r, ps_r_i, etc.

2MASS Photometry:

• j_m, h_m, k_m (float64): 2MASS magnitudes
• j_h, h_k, j_k (float64): 2MASS colors

Missing Values: All missing values are encoded as -999.0 for consistency.

Final Catalog with Predictions

File: catalogs/stars_types_with_best_predictions.fits Size: 196 MB Format: FITS binary table

Complete catalog of 2.1M eclipsing binaries with:

• Original Gaia temperatures (where available)
• ML-predicted temperatures (best-of-three ensemble)
• Prediction uncertainties
• Quality flags (A=Gaia, B/C/D=ML by uncertainty, X=none)

Coverage: 97.2% of stars have Teff values (58.3% Gaia original + 38.9% ML predictions)

Usage

Download with Python

from huggingface_hub import hf_hub_download
import polars as pl

# Download unified photometry
file_path = hf_hub_download(
    repo_id="Dedulek/gaia-eb-teff-datasets",
    filename="photometry/eb_unified_photometry.parquet",
    repo_type="dataset"
)

# Load with Polars (recommended for large datasets)
df = pl.read_parquet(file_path)

# Or with Pandas
import pandas as pd
df = pd.read_parquet(file_path)

print(f"Loaded {len(df)} eclipsing binaries")
print(f"Columns: {df.columns}")

Download with Hugging Face CLI

# Install CLI
pip install huggingface_hub

# Download specific file
huggingface-cli download Dedulek/gaia-eb-teff-datasets \
    --repo-type dataset \
    --include "photometry/eb_unified_photometry.parquet" \
    --local-dir ./data

Training Example

import polars as pl
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split

# Load data
df = pl.read_parquet("eb_unified_photometry.parquet")

# Filter stars with known Teff and Gaia photometry
df_train = df.filter(
    (pl.col("teff_gaia") != -999.0) &
    (pl.col("bp_rp") != -999.0)
)

# Prepare features and target
features = ["g", "bp", "rp", "bp_rp", "g_bp", "g_rp"]
X = df_train[features].to_numpy()
y = df_train["teff_gaia"].to_numpy()

# Train model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = RandomForestRegressor(n_estimators=300, max_depth=20, random_state=42)
model.fit(X_train, y_train)

# Evaluate
score = model.score(X_test, y_test)
print(f"R² score: {score:.3f}")

Dataset Statistics

Photometric Coverage

Survey	Coverage	N_stars
Gaia DR3	100%	2,179,680
Pan-STARRS DR1	53.5%	1,166,000
2MASS (J)	~60%	~1,300,000
2MASS (H)	~60%	~1,300,000
2MASS (K)	~60%	~1,300,000

Stellar Parameter Coverage

Parameter	Coverage	Mean	Std	Range
Teff (Gaia)	58%	7,450 K	3,200 K	2,500 - 50,000 K
log(g)	56%	3.8	0.5	0.5 - 5.5
[Fe/H]	48%	-0.2	0.4	-2.5 - +0.5

Temperature Distribution

Teff Range	N_stars	Percentage
< 4,000 K (Cool)	180,000	14%
4,000-6,000 K (Mid)	520,000	41%
6,000-10,000 K (Hot)	450,000	36%
> 10,000 K (Very Hot)	115,000	9%

Data Quality

Missing Value Convention

All surveys use -999.0 to indicate missing values. Always filter these before analysis:

# Filter valid measurements
df_clean = df.filter(
    (pl.col("bp_rp") != -999.0) &
    (pl.col("teff_gaia") != -999.0)
)

Known Issues

1. Gaia GSP-Phot Bias: Systematic underestimation of Teff for hot stars (>10,000 K)

   • Correction coefficients available in model repository
   • See: data/teff_correction_coeffs_deg2.pkl

2. Pan-STARRS Coverage: Northern hemisphere bias (Dec > -30°)

3. 2MASS Saturation: Bright stars (J < 6) may be saturated

Model Performance

Pre-trained models achieve the following performance on held-out test sets:

Model	Features	MAE (K)	RMSE (K)	R²	Within 10%
Gaia Colors (Log)	6 Gaia colors/bands	557	1,021	0.640	68.5%
Gaia + 2MASS	5 optical+IR colors	765	1,168	0.315	43.4%
Best-of-Three Ensemble	Multiple models	263	-	-	-

Citation

If you use this dataset, please cite:

@dataset{gaia_eb_teff_2025,
  author = {Your Name},
  title = {Gaia Eclipsing Binary Effective Temperature Datasets},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/Dedulek/gaia-eb-teff-datasets}
}

Data Sources

Please also cite the original surveys:

Gaia DR3:

@article{gaia2023,
  author = {{Gaia Collaboration}},
  title = {Gaia Data Release 3},
  journal = {Astronomy & Astrophysics},
  year = {2023},
  volume = {674},
  pages = {A1}
}

Pan-STARRS:

@article{panstarrs2020,
  author = {Flewelling, H. A. and others},
  title = {The Pan-STARRS1 Database and Data Products},
  journal = {The Astrophysical Journal Supplement Series},
  year = {2020},
  volume = {251},
  pages = {7}
}

2MASS:

@article{2mass2006,
  author = {Skrutskie, M. F. and others},
  title = {The Two Micron All Sky Survey (2MASS)},
  journal = {The Astronomical Journal},
  year = {2006},
  volume = {131},
  pages = {1163}
}

License

This dataset is released under CC BY 4.0 (Creative Commons Attribution 4.0 International).

You are free to:

• Share: copy and redistribute the material
• Adapt: remix, transform, and build upon the material

Under the following terms:

• Attribution: You must give appropriate credit and indicate if changes were made

Contact

For questions or issues with this dataset:

• Open an issue on the GitHub repository
• Contact: [email protected]

Acknowledgments

This work has made use of data from:

• ESA mission Gaia (https://www.cosmos.esa.int/gaia)
• Pan-STARRS (https://panstarrs.stsci.edu/)
• 2MASS (https://www.ipac.caltech.edu/2mass/)

Special thanks to the astronomical community for making these datasets publicly available.