Age Determination of LAMOST Red Giant Branch Stars Based on the Gradient Boosting Decision Tree Method

Abstract

In this study, we estimate the stellar ages of LAMOST DR8 red giant branch (RGB) stars based on the gradient boosting decision tree (GBDT) algorithm. We used 2643 RGB stars extracted from the APOKASC-2 asteroseismological catalog as the training data set. After selecting the parameters ([α/Fe], [C/Fe], T eff, [N/Fe], [C/H], log g) highly correlated with age using GBDT, we apply the same GBDT method to the new catalog of more than 590,000 stars classified as RGB stars. The test data set shows that the median relative error is around 11.6% for the method. We also compare the predicted ages of RGB stars with other studies (e.g., based on APOGEE) and find some systematic differences. The final uncertainty is about 15%–30% compared to the ages of open clusters. Then, we present the spatial distribution of the RGB sample with an age determination, which could recreate the expected result, and discuss systematic biases. All these diagnostics show that one can apply the GBDT method to other stellar samples to estimate atmospheric parameters and age.