Data-Driven Selection and Spectral Classification of White Dwarf Stars

TL;DR

This paper introduces a fast, automated pipeline for selecting and classifying white dwarf stars from large spectroscopic surveys, significantly improving accuracy and enabling rapid processing of vast data sets.

Contribution

The authors develop a novel data-driven pipeline that automates white dwarf classification with high accuracy, surpassing existing methods, and provide a large catalog of classified WD spectra.

Findings

Achieved over 90% accuracy in classifying WD types

Successfully identified 424,096 high-confidence WD candidates

Provided the first automated catalog of WD spectra with quantifiable classifications

Abstract

The next generation of spectroscopic surveys is expected to provide spectra for hundreds of thousands of white dwarf (WD) candidates in the upcoming years. Currently, spectroscopic classification of white dwarfs is mostly done by visual inspection, requiring substantial amounts of expert attention. We propose a data-driven pipeline for fast, automatic selection and spectroscopic classification of WD candidates, trained using spectroscopically confirmed objects with available Gaia astrometry, photometry, and Sloan Digital Sky Survey (SDSS) spectra with signal-to-noise ratios $\geq9$. The pipeline selects WD candidates with improved accuracy and completeness over existing algorithms, classifies their primary spectroscopic type with $\gtrsim 90\%$ accuracy, and spectroscopically detects main sequence companions with similar performance. We apply our pipeline to the Gaia Data Release 3 cross-matched with the SDSS Data Release 17 (DR17), identifying 424 096 high-confidence WD candidates and providing the first catalogue of automated and quantifiable classification for 36 523 WD spectra. Both the catalogue and pipeline are made available online. Such a tool will prove particularly useful for the undergoing SDSS-V survey, allowing for rapid classification of thousands of spectra at every data release.