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arXiv · 2411.14714

Mining double-line spectroscopic candidates in the LAMOST medium-resolution spectroscopic survey using human-AI hybrid method

Shan-shan Li·Chun-qian Li·Chang-hua Li·Dong-wei Fan·Yun-fei Xu·Lin-ying Mi·Chen-zhou Cui·Jian-rong Shi
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Abstract

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.

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Shan-shan Li, Chun-qian Li, Chang-hua Li, Dong-wei Fan, Yun-fei Xu, Lin-ying Mi, Chen-zhou Cui, Jian-rong Shi. 2024-11-22. Mining double-line spectroscopic candidates in the LAMOST medium-resolution spectroscopic survey using human-AI hybrid method. https://arxiv.org/abs/2411.14714

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