Newly spectroscopically confirmed DB white dwarfs

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 data set.