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

Autor:	Li, Shan-shan, Li, Chun-qian, Li, Chang-hua, Fan, Dong-wei, Xu, Yun-fei, Mi, Lin-ying, Cui, Chen-zhou, Shi, Jian-rong
Rok vydání:	2024
Předmět:	Astrophysics - Instrumentation and Methods for Astrophysics Astrophysics - Astrophysics of Galaxies Astrophysics - Solar and Stellar Astrophysics
Druh dokumentu:	Working Paper
Popis:	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. Comment: 18 pages, 11 figures, accepted by ApJS, Data available via China-VO PaperData repository
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.14714 Zobrazit plný text záznamu View this record from Arxiv