SIMBAD references

2018PASP..130h4203K - Publ. Astron. Soc. Pac., 130, part no 8, 4203-84203 (2018/August-0)

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

KONG X., LUO A.-L., LI X.-R., WANG Y.-F., LI Y.-B. and ZHAO J.-K.

Abstract (from CDS):

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 data set. 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.

Abstract Copyright: © 2018. The Astronomical Society of the Pacific. All rights reserved.

Journal keyword(s):

VizieR on-line data: <Available at CDS (J/PASP/130/h4203): table6.dat>

CDS comments: Only objects newly identified in this paper have been ingested in SIMBAD. SDSS J020022.48+242343.3 and SDSS J020910.57-043943.1 were not found.

Simbad objects: 56

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