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Mass and Age Determination of the LAMOST Data with Different Machine-learning Methods

Abstract : We present a catalog of 948,216 stars with mass labels and a catalog of 163,105 red clump (RC) stars with mass and age labels simultaneously. The training data set is crossmatched from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR5, and high-resolution asteroseismology data, mass, and age are predicted by the random forest (RF) method or a convex-hull algorithm. The stellar parameters with a high correlation with mass and age are extracted and the test data set shows that the median relative error of the prediction model for the mass of the large sample is 3%, and for the mass and age of RC stars is 4% and 7%. We also compare the predicted age of RC stars with recent works and find that the final uncertainty of the RC sample could reach 18% for age and 9% for mass; meanwhile, the final precision of the mass for the large sample with different types of stars could reach 13% without considering systematics. All of this implies that this method could be widely used in the future. Moreover, we explore the performance of different machine-learning methods for our sample, including Bayesian linear regression and the gradient-boosting decision tree (GBDT), multilayer perceptron, multiple linear regression, RF, and support vector regression methods. Finally, we find that the performance of a nonlinear model is generally better than that of a linear model, and the GBDT and RF methods are relatively better.
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https://hal-insu.archives-ouvertes.fr/insu-03849377
Contributor : Nathalie POTHIER Connect in order to contact the contributor
Submitted on : Friday, November 11, 2022 - 5:47:11 PM
Last modification on : Saturday, November 12, 2022 - 3:45:07 AM

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Distributed under a Creative Commons Attribution 4.0 International License

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Qi-Da Li, Hai-Feng Wang, Yang-Ping Luo, Qing Li, Li-Cai Deng, et al.. Mass and Age Determination of the LAMOST Data with Different Machine-learning Methods. The Astrophysical Journal Supplement Series, 2022, 262, ⟨10.3847/1538-4365/ac81be⟩. ⟨insu-03849377⟩

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