Mining double-line spectroscopic candidates in the LAMOST medium-resolution spectroscopic survey using human-AI hybrid method
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912130342584320 |
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| author | Li, Shan-shan Li, Chun-qian Li, Chang-hua Fan, Dong-wei Xu, Yun-fei Mi, Lin-ying Cui, Chen-zhou Shi, Jian-rong |
| author_facet | Li, Shan-shan Li, Chun-qian Li, Chang-hua Fan, Dong-wei Xu, Yun-fei Mi, Lin-ying Cui, Chen-zhou Shi, Jian-rong |
| contents | We utilize a hybrid approach that integrates the traditional cross-correlation function (CCF) and machine learning to detect spectroscopic multi-systems, specifically focusing on double-line spectroscopic binary (SB2). Based on the ninth data release (DR9) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), which includes a medium-resolution survey (MRS) containing 29,920,588 spectra, we identify 27,164 double-line and 3124 triple-line spectra, corresponding to 7096 SB2 candidates and 1903 triple-line spectroscopic binary (SB3) candidates, respectively, representing about 1% of the selection dataset from LAMOST-MRS DR9. Notably, 70.1% of the SB2 candidates and 89.6% of the SB3 candidates are newly identified. Compared to using only the traditional CCF technique, our method significantly improves the efficiency of detecting SB2, saves time on visual inspections by a factor of four. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14714 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mining double-line spectroscopic candidates in the LAMOST medium-resolution spectroscopic survey using human-AI hybrid method Li, Shan-shan Li, Chun-qian Li, Chang-hua Fan, Dong-wei Xu, Yun-fei Mi, Lin-ying Cui, Chen-zhou Shi, Jian-rong Instrumentation and Methods for Astrophysics Astrophysics of Galaxies Solar and Stellar Astrophysics We utilize a hybrid approach that integrates the traditional cross-correlation function (CCF) and machine learning to detect spectroscopic multi-systems, specifically focusing on double-line spectroscopic binary (SB2). Based on the ninth data release (DR9) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), which includes a medium-resolution survey (MRS) containing 29,920,588 spectra, we identify 27,164 double-line and 3124 triple-line spectra, corresponding to 7096 SB2 candidates and 1903 triple-line spectroscopic binary (SB3) candidates, respectively, representing about 1% of the selection dataset from LAMOST-MRS DR9. Notably, 70.1% of the SB2 candidates and 89.6% of the SB3 candidates are newly identified. Compared to using only the traditional CCF technique, our method significantly improves the efficiency of detecting SB2, saves time on visual inspections by a factor of four. |
| title | Mining double-line spectroscopic candidates in the LAMOST medium-resolution spectroscopic survey using human-AI hybrid method |
| topic | Instrumentation and Methods for Astrophysics Astrophysics of Galaxies Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2411.14714 |