Spectroscopic ages for 4 million main-sequence dwarf stars from LAMOST DR10 estimated with data-driven approach
Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Jiwei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Jifeng Liu

TL;DR
This study develops a data-driven method using LAMOST spectra and machine learning to estimate ages for 4 million main-sequence dwarf stars, enabling large-scale stellar age analysis.
Contribution
It introduces a novel approach combining wide binary calibration and XGBoost to accurately determine dwarf star ages from spectral data.
Findings
Achieves 10-25% age precision for high signal-to-noise K-type stars.
Creates a massive, publicly accessible age catalog for 4 million dwarf stars.
Demonstrates chemical abundance features as effective stellar age indicators.
Abstract
Stellar age determination for large samples of stars opens new avenues for a broad range of astronomical sciences. While precise stellar ages for evolved stars have been derived from large ground- and space-based stellar surveys, reliable age determination for cool main-sequence dwarf stars remains a challenge. In this work, we set out to estimate the age of dwarf stars from the LAMOST spectra with a data-driven approach. We build a training set by using wide binaries that the primary component has reliable isochrone age estimate thus gives the age of the secondary. This training set is further supplemented with field stars and cluster stars whose ages are known. We then train a data-driven model for inferring age from their spectra with the XGBoost algorithm. Given a spectral signal-to-noise ratio greater than 50, the age estimation precise to 10% to 25% for K-type stars, as younger…
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