Work

Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX

Year: 2025

Type: preprint

Abstract: The statistical characteristics of double main-sequence (MS) binaries are essential for investigating star formation, binary evolution, and population synthesis. Our previous study proposed a machine learning-based method to identify MS binaries from MS single stars using mock data from the Chinese Space Station Telescope (CSST). We further utilized detection efficiencies and an empirical mass ratio distribution to estimate the binary fraction within the sample. To further validate the effectiveness of this method, we conducted a more realistic sample simulation, incorporating additional factors such as metallicity, extinction, and photometric errors from CSST simulations. The detection efficiency for binaries with mass ratios between 0.2 and 0.7 reached over 80%. We performed a detailed observational validation using the data selected from the Gaia Sky Survey and Galaxy Evolution Explorer. The detection efficiency for MS binaries in the observed sample was 65%. The binary fraction can be inferred with high precision for a set of observed samples, based on accurate empirical mass ratio distribution.

Source: arXiv (Cornell University)

Authors: Li, Jia-jia, Xiong, Jian-ping, Tian, Zhi-jia, Liu, Chao, Han, Zhan-wen, Chen, Xue-fei

Institutions: Chinese Academy of Sciences, Yunnan Observatories, University of Chinese Academy of Sciences, Yunnan University, National Astronomical Observatories

Language: English

Topic: Stellar, planetary, and galactic studies

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: G1033825943, G1682633203, G2241947381, G2708569376, G3578086565, G6676612109, G7195374644, G8518170077

OpenAlex ID: W6979367269

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