摘要
Symbiotic stars are interacting binary systems, making them valuable for studying various astronomical phenomena, such as stellar evolution, mass transfer, and accretion processes. Despite recent progress in the discovery of symbiotic stars, a significant discrepancy between the observed population of symbiotic stars and the number predicted by theoretical models. To bridge this gap, this study utilized machine learning techniques to efficiently identify new symbiotic star candidates. Three algorithms(XGBoost, LightGBM, and Decision Tree)were applied to a data set of 198 confirmed symbiotic stars and the resulting model was then used to analyze data from the LAMOST survey, leading to the identification of 11,709 potential symbiotic star candidates. Out of these potential symbiotic star candidates listed in the catalog, 15 have spectra available in the Sloan Digital Sky Survey(SDSS) survey. Among these 15 candidates, two candidates, namely V^(*)V603 Ori and V^(*)GN Tau, have been confirmed as symbiotic stars. The remaining 11 candidates have been classified as accreting-only symbiotic star candidates. The other two candidates, one of which has been identified as a galaxy by both SDSS and LAMOST surveys, and the other identified as a quasar by SDSS survey and as a galaxy by LAMOST survey.
基金
the generous support of the Natural Science Foundation of Xinjiang No. 2021D01C075
the National Natural Science Foundation of China, project Nos. 12163005, 12003025, U2031204, 11863005, and 12288102
the science research grants from the China Manned Space Project with No. CMS-CSST-2021-A10
the Scientific Research Program of the Higher Education Institution of Xinjiang (No. XJEDU2022P003)
supported by China National Astronomical Data Center (NADC) and Chinese Virtual Observatory (China-VO)
supported by Astronomical Big Data Joint Research Center, co-founded by National Astronomical Observatories, Chinese Academy of Sciences and Alibaba Cloud
This publication makes use of data products from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/ California Institute of Technology
NEOWISE, which is a project of the Jet Propulsion Laboratory/California Institute of Technology
WISE and NEOWISE are funded by the National Aeronautics and Space Administration
This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology
funded by the National Aeronautics and Space Administration and the National Science Foundation
Guo Shou Jing Telescope (the Large Sky Area MultiObject Fiber Spectroscopic Telescope LAMOST) is a National Major Scientific Project built by the Chinese Academy of Sciences
Funding for the project has been provided by the National Development and Reform Commission
Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions
support and resources from the Center for High Performance Computing at the University of Utah。
