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UID:pretalx-adass2026-FLP3BT@pretalx.adass.org
DTSTART;TZID=AWST:20261103T163000
DTEND;TZID=AWST:20261103T164500
DESCRIPTION:Spectroscopic binaries and multiple-star systems are key sample
 s for studying stellar formation\, evolution\, and dynamical interactions.
  However\, identifying such systems in large spectroscopic surveys remains
  challenging because of their rarity\, complex blended spectral features\,
  and the high cost of visual inspection.\nIn this talk\, I present a Human
 -AI hybrid approach for searching double-lined and triple-lined spectrosco
 pic multiple systems in the LAMOST Medium-Resolution Spectroscopic Survey.
  The method combines the cross-correlation function (CCF)\, machine-learni
 ng classification\, and human verification to improve the efficiency and r
 eliability of candidate selection. I also compare several classifiers\, in
 cluding SVM\, Transformer\, DNN\, CNN\, and LSTM\, under different trainin
 g-data configurations\, and evaluate their generalization ability using bo
 th observational and independent test sets.\nFinally\, I introduce how the
  candidate catalogs\, CCF features\, machine-learning results\, and multi-
 source information from Gaia and existing binary/multiple-star catalogs ar
 e integrated into the China-VO multi-star system archive. The platform is 
 designed not only as a data repository\, but also as a tool for scientific
  discovery: it supports candidate cross-identification\, visualization of 
 spectra and classification results\, identification of newly discovered sy
 stems\, selection of special targets\, and prioritization for follow-up ob
 servations. By combining large-scale survey mining with an online research
  platform\, this work provides a practical pathway from spectroscopic cand
 idate detection to scientific validation of multiple-star systems.
DTSTAMP:20261001T111403Z
LOCATION:Banquet Hall
SUMMARY:Mining Double-line Spectroscopic Candidates in the LAMOST Medium-re
 solution Spectroscopic Survey Using a Human–AI Hybrid Method - Shanshan 
 Li
URL:https://pretalx.adass.org/adass2026/talk/FLP3BT/
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