White dwarfs identified in LAMOST Data Release 5

TL;DR

This paper reports the identification and characterization of thousands of white dwarfs from LAMOST Data Release 5, including new discoveries and parameter estimations, using machine learning and visual verification methods.

Contribution

It introduces a comprehensive method combining pipeline classification, colour cuts, and machine learning to identify and analyze white dwarfs in LAMOST DR5, including new discoveries.

Findings

Identified 2,625 DA and 182 DB spectra in DR5

Estimated stellar parameters for high S/N white dwarfs

Discovered 393 new DA and 46 new DB white dwarfs

Abstract

In this paper, we report white dwarfs identified in the 5th Data Release of the Large Area Multi-Object fibre Spectroscopic Telescope, including spectral types of DA, DB, DC, DZ, and so on. There are 2 625 DA spectra of 2 281 DA stars, 182 DB spectra of 166 DB stars, 62 DC spectra of 58 DC stars, 36 DZ spectra of 33 DZ stars and many other types identified, in addition to our previous paper (Data Release 2). Among those sources, 393 DA stars and 46 DB stars are new identifications after cross-matching with the literature. In order to select DA candidates, we use the classification result from the LAMOST pipeline, colour-colour cut method and a random forest machine learning method. For DBs, since there is no template for DB in the pipeline model, a random forest machine learning method is chosen to select candidates. All the WD candidates have been visually checked individually. The parameters of effective temperature, surface gravity, mass, and cooling age have been estimated for relatively high signal-to-noise ratio DAs and DBs. The peaks of the DA and DB mass distributions are found to be around 0.62Msun and 0.65Msun, respectively. Finally, the data and method we used to select white dwarf candidates for the second phase of LAMOST survey are also addressed in this paper.