Bootstrap-Ensembled Neural Networks for Robust Stellar Temperature and Age Predictions
Marco Tarantino, L. Prisinzano, Nicoletta D’Angelo, Francesco Damiani, Giada Adelfio · 2025
This study presents a machine learning approach to predict the effective temperatures and their accuracy of premain sequence stars, which are essential for deriving stellar ages through isochrone fitting and starspot-dependent evolutionary models. We trained a Neural Network on high-quality spectroscopic temperatures from the Gaia-ESO Survey, using Gaia DR3 and 2MASS photometry as input features. To estimate predictive uncertainty, we implemented a Neural Network with bootstrap procedure, where each model was warm-started using the parameters of an initial network trained on the full dataset with K-fold cross-validation. This allowed accurate and robust temperature predictions for large stellar populations lacking spectroscopic data, with strong performance in the lowtemperature regime. The predicted temperatures were used to build Hertzsprung-Russell diagrams and derive stellar ages of young clusters by starspot evolutionary models, achieving good agreement with spectroscopic benchmarks and independent methods, such as lithium equivalent widths.