Identifying symbiotic stars with machine learning (Yongle+, 2023) ================================================================================ Identifying symbiotic star candidates with machine learning Yongle Jia, Sufen Guo, Chunhua Zhu, Lin Li, Mei Ma, Guoliang Lv ================================================================================ Keywords: binaries: symbiotic --- techniques: spectroscopic --- binaries: spectroscopic --- methods: data analysis Abstract: 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 stars candidates. Three algorithms (XGBoost, LightGBM, and Decision Tree) were applied to a dataset 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 stars candidates. Out of the these potential symbiotic stars candidates listed in the catalog, 15 have spectra available in the 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. Description: We utilized machine learning techniques to efficiently identify new symbiotic stars. File 11709.csv shows the 11709 symbiotic star candidates predicted by cross-matching LAMOST with AllWISE. File 15.csv shows the above 11709 sources that have spectra in the SDSS for 15 sources. We have provided the relevant coordinates and magnitude information in the catalog. ------------------------------------------------------------------------------ Note: The catalog describes each metadata as follows: ------------------------------------------------------------------------------ Marker Description ------------------------------------------------------------------------------ LAMOST_designation Target designation of LAMOST RA Right ascension of object from LAMOST DEC Declination of object from LAMOST LAMOST_class The spectrum type determined by LAMOST SDSS_class The spectrum type determined by SDSS subclass The spectrum type determined by our work AllWISE_designation Target designation of AllWISE RAJ2000 Right ascension of object from AllWISE DEJ2000 Declination of object from AllWISE W1mag The magnitude of the W1 band W2mag The magnitude of the W2 band W3mag The magnitude of the W3 band W4mag The magnitude of the W4 band Jmag The magnitude of the J band Hmag The magnitude of the H band Kmag The magnitude of the K band RA_SDSS Right ascension of object from SDSS DEC_SDSS Declination of object from SDSS plate an integer indicating which SDSS plug plate was used to collect the spectrum mjd an integer denoting the Modified Julian Date of the night when the observation was carried out. Some plates were observed more than once; these different observations will have different MJD values. fiberid an integer denoting the fiber number (1 to 640 for SDSS-I/II; 1 to 1000 for BOSS) ------------------------------------------------------------------------------