Identify main-sequence binaries from the Chinese Space Station Telescope Survey with machine learning
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913774844248064 |
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| author | Li, Jia-jia Wang, Jin-liang Ji, Kai-fan Liu, Chao Chen, Hai-liang Han, Zhan-wen Chen, Xue-fei |
| author_facet | Li, Jia-jia Wang, Jin-liang Ji, Kai-fan Liu, Chao Chen, Hai-liang Han, Zhan-wen Chen, Xue-fei |
| contents | The statistical properties of double main sequence (MS) binaries are very important for binary evolution and binary population synthesis. To obtain these properties, we need to identify these MS binaries. In this paper, we have developed a method to differentiate single MS stars from double MS binaries from the Chinese Space Station Telescope (CSST) Survey with machine learning. This method is reliable and efficient to identify binaries with mass ratios between 0.20 and 0.80, which is independent of the mass ratio distribution. But the number of binaries identified with this method is not a good approximation to the number of binaries in the original sample due to the low detection efficiency of binaries with mass ratios smaller than 0.20 or larger than 0.80. Therefore, we have improved this point by using the detection efficiencies of our method and an empirical mass ratio distribution and then can infer the binary fraction in the sample. Once the CSST data are available, we can identify MS binaries with our trained multi-layer perceptron model and derive the binary fraction of the sample. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02232 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identify main-sequence binaries from the Chinese Space Station Telescope Survey with machine learning Li, Jia-jia Wang, Jin-liang Ji, Kai-fan Liu, Chao Chen, Hai-liang Han, Zhan-wen Chen, Xue-fei Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics The statistical properties of double main sequence (MS) binaries are very important for binary evolution and binary population synthesis. To obtain these properties, we need to identify these MS binaries. In this paper, we have developed a method to differentiate single MS stars from double MS binaries from the Chinese Space Station Telescope (CSST) Survey with machine learning. This method is reliable and efficient to identify binaries with mass ratios between 0.20 and 0.80, which is independent of the mass ratio distribution. But the number of binaries identified with this method is not a good approximation to the number of binaries in the original sample due to the low detection efficiency of binaries with mass ratios smaller than 0.20 or larger than 0.80. Therefore, we have improved this point by using the detection efficiencies of our method and an empirical mass ratio distribution and then can infer the binary fraction in the sample. Once the CSST data are available, we can identify MS binaries with our trained multi-layer perceptron model and derive the binary fraction of the sample. |
| title | Identify main-sequence binaries from the Chinese Space Station Telescope Survey with machine learning |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2504.02232 |