SN 2021ihf , the SIMBAD biblio

2025PASP..137i4001D - Publ. Astron. Soc. Pac., 137, part no 9, 4001 (2025/September-0)

STag. II. Classification of Serendipitous Supernovae Observed by Galaxy Redshift Surveys.

DAVISON W., PARKINSON D., BENZVI S., PALMESE A., AGUILAR J., AHLEN S., BROOKS D., CLAYBAUGH T., DE LA MACORRA A., DEY A., DOEL P., GAZTANAGA E., GONTCHO A GONTCHO S., HOWLETT C., JUNEAU S., KISNER T., KREMIN A., LAMBERT A., LANDRIAU M., LE GUILLOU L., MEISNER A., MIQUEL R., MOUSTAKAS J., MYERS A.D., POPPETT C., PRADA F., REZAIE M., ROSSI G., SANCHEZ E., SCHLAFLY E.F., SCHUBNELL M., SPRAYBERRY D., TARLE G., WEAVER B.A. and ZOU H.

Abstract (from CDS):

With the number of supernovae observed expected to drastically increase thanks to large-scale surveys like the Dark Energy Spectroscopic Instrument (DESI), it is necessary that the tools we use to classify these objects keep up with this increase. We previously created Supernova Tagging and Classification (STag) to address this problem by employing machine learning techniques alongside logistic regression in order to assign “tags” to spectra based on spectral features. STag II is a continuation of this work, which now makes use of model supernova spectra combined with real DESI spectra in order to train STag to better deal with realistic data. We also make use of the rlap score as a trustworthiness cut, making for a more robust and accurate supernova classifier than before.

Abstract Copyright: © 2025. The Astronomical Society of the Pacific. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

Journal keyword(s): Supernovae - Astrostatistics techniques - Classification

Simbad objects: 26

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