Spectral feature extraction for DB white dwarfs through machine learning applied to new discoveries in the SDSS DR12 and DR14

TL;DR

This paper presents a machine learning approach combining feature extraction and classification to identify and analyze DB white dwarfs in SDSS data, leading to new discoveries and a comprehensive catalog.

Contribution

The study introduces a novel ML pipeline using LASSO and SVM for spectral feature extraction and classification of DB white dwarfs in SDSS data, resulting in new discoveries.

Findings

Identified 58 new DB white dwarfs and one AM CVn.

Confirmed 2029 objects spectroscopically.

Compiled a catalog with stellar parameters.

Abstract

Using a machine learning (ML) method, we mine DB white dwarfs (DBWDs) from the Sloan Digital Sky Survey (SDSS) Data Release (DR) 12 and DR14. The ML method consists of two parts: feature extraction and classification. The least absolute shrinkage and selection operator (LASSO) is used for the spectral feature extraction by comparing high quality data of a positive sample group with negative sample groups. In both the training and testing sets, the positive sample group is composed of a selection of 300 known DBWDs, while the negative sample groups are obtained from all types of SDSS spectra. In the space of the LASSO detected features, a support vector machine is then employed to build classifiers that are used to separate the DBWDs from the non DBWDs for each individual type. Depending on the classifiers, the DBWD candidates are selected from the entire SDSS dataset. After visual inspection, 2808 spectra (2029 objects) are spectroscopically confirmed. By checking the samples with the literature, there are 58 objects with 60 spectra that are newly identified, including a newly discovered AM CVn. Finally, we measure their effective temperatures (Teff), surface gravities (log g), and radial velocities, before compiling them into a catalog.