Gaia Eclipsing Binary Effective Temperature Models
Pre-trained Random Forest models and a soft PCA–KDE ensemble for predicting (T_{\mathrm{eff}}) of Gaia DR3 eclipsing binaries from photometry, extinction, and EB catalogue quantities (no parallax). Training labels are polynomial-corrected GSP-Phot temperatures (hot-star blend above 10 000 K).
Code: gabdeevmaksim/gaia_eb_rf_teff
Production models (July 2026)
| Registry key | File stem | Role |
|---|---|---|
gaia_teff_flag1_ebv_freq |
rf_gaia_teff_flag1_ebv_freq_optuna_20260712_184632 |
Flag=1 labels |
gaia_teff_base_ebv_freq |
rf_gaia_teff_base_ebv_freq_optuna_20260712_195904 |
Photometry + (E(B-V)) + frequency |
gaia_teff_cluster_ebv_freq |
rf_gaia_teff_cluster_ebv_freq_optuna_20260712_232723 |
+ soft (K{=}5) memberships |
gaia_teff_eb_params_production |
rf_gaia_teff_eb_params_production_optuna_20260713_014917 |
+ EB light-curve params |
Ensemble: ensemble_selector_gaia_teff_production_soft_kde_20260715_140546.pkl
(soft weights in a 2D PCA plane; MSC off; EB-params out-of-coverage fallback).
Soft-KDE holdout (common ~28k sample)
| Metric | Value |
|---|---|
| MAE | 675 K |
| MedAE | 311 K |
| Within 10% | 68.6% |
Catalogue: 847 486 ML fills; combined with GSP-Phot → 97.9% Teff coverage of (2,169,721) cross-matched EBs. Do not use older “263 K Best-of-Three” claims from earlier Hub versions.
Quick download
pip install huggingface_hub joblib scikit-learn polars
# From the git repo:
python scripts/download_datasets.py --models all
from huggingface_hub import hf_hub_download
import joblib
path = hf_hub_download(
repo_id="Dedulek/gaia-eb-teff-models",
filename="rf_gaia_teff_base_ebv_freq_optuna_20260712_195904.pkl",
repo_type="model",
)
model = joblib.load(path)
Features for the base/flag1 models (order matters):
g, bp, rp, bp_rp, g_bp, g_rp, ebv_sandf, frequency. Targets are trained in
(\log_{10}(T_{\mathrm{eff}}/\mathrm{K})); invert with 10 ** pred.
Cluster and EB-params models need their sidecar .pkl files (kmeans/scaler or
categorical encoder) listed in model_registry.yaml.
Legacy
Older Nov‑2025 models (gaia_teff_corrected_log, 2MASS, etc.) may remain under
legacy/ on this Hub for reproducibility of prior experiments. Prefer the
July 2026 production quartet + soft-KDE selector for new work.
Citation
Please cite the accompanying paper and this repository when using these models. Gaia data remain subject to ESA archive terms.