Xiaokun Hou, Wenbo Wu, Gang Zhao, Haining Li, Jingkun Zhao

Stellar age is a fundamental quantity for Galactic archaeology, but reliable age estimation for large stellar samples remains challenging. In this work, we develop an uncertainty aware NGBoost framework for stellar age estimation using Gaia XP-derived atmospheric parameters and chemical abundances. Different from the standard NGBoost model, we modify the loss function by incorporating the uncertainties of the training age labels. We further use a Monte Carlo strategy to quantify the influence of input-feature uncertainties on the predicted ages. The resulting model provides age estimates together with uncertainty estimates. Applying this framework to Gaia XP stars, we construct a stellar age catalog containing 15,175,107 stars.