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

TL;DR

This paper presents a hybrid human-AI method combining cross-correlation and machine learning to efficiently identify double- and triple-line spectroscopic binaries in the LAMOST medium-resolution survey, discovering many new candidates.

Contribution

The study introduces a novel hybrid approach that enhances detection efficiency of spectroscopic binary candidates in large survey data, outperforming traditional methods.

Findings

Identified 27,164 double-line spectra and 3,124 triple-line spectra from nearly 30 million LAMOST spectra.

Discovered over 70% of SB2 and nearly 90% of SB3 candidates are new.

Method reduces detection time and visual inspection effort by a factor of four.

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.