摘要
In this study, we employ machine learning to build a catalog of DB white dwarfs(DBWDs) from the LAMOST Data Release(DR) 5. Using known DBs from SDSS DR14, we selected samples of highquality DB spectra from the LAMOST database and applied them to train the machine learning process.Following the recognition procedure, we chose 351 DB spectra of 287 objects, 53 of which were new identifications. We then utilized all the DBWD spectra from both SDSS DR14 and LAMOST DR5 to construct DB templates for LAMOST 1 D pipeline reductions. Finally, by applying DB parameter models provided by D. Koester and the distance from Gaia DR2, we calculated the effective temperatures, surface gravities and distributions of the 3 D locations and velocities of all DBWDs.
In this study, we employ machine learning to build a catalog of DB white dwarfs(DBWDs) from the LAMOST Data Release(DR) 5. Using known DBs from SDSS DR14, we selected samples of highquality DB spectra from the LAMOST database and applied them to train the machine learning process.Following the recognition procedure, we chose 351 DB spectra of 287 objects, 53 of which were new identifications. We then utilized all the DBWD spectra from both SDSS DR14 and LAMOST DR5 to construct DB templates for LAMOST 1 D pipeline reductions. Finally, by applying DB parameter models provided by D. Koester and the distance from Gaia DR2, we calculated the effective temperatures, surface gravities and distributions of the 3 D locations and velocities of all DBWDs.
基金
funded by the National Basic Research Program of China (973 program, 2014CB845700)
the National Natural Science Foundation of China (Grant No. 11390371/4)
The 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
provided by the National Development and Reform Commission
