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

Abstract 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 r lap score as a trustworthiness cut, making for a more robust and accurate supernova classifier than before.

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Publication Details

Journal
Publications of the Astronomical Society of the Pacific
Published
2025-09-01
DOI
https://doi.org/10.1088/1538-3873/ae015d
Primary Topic
Gamma-ray bursts and supernovae
Type
article
Field-Weighted Citation Impact
0.00

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article

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

S. Juneau, Graziano Rossi, Gregory Tarlé, Laurent Le Guillou et al.
Publications of the Astronomical Society of the Pacific
Gamma-ray bursts and supernovae
article

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

S. Juneau, Graziano Rossi, Gregory Tarlé, Laurent Le Guillou, David Sprayberry, Daniel I. Brooks, Axel de la Macorra, Martin Landriau, David Parkinson, A. Palmese, Adam Lambert, J. Aguilar, Anthony Kremin, Eusebio Sanchez, Satya Gontcho A Gontcho, S. BenZvi, ENRIQUE GAZTAÑAGA, Edward F. Schlafly, Cullan Howlett, Arjun T. Dey, Steven P. Ahlen, Aaron Meisner, R. Miquel, Theodore S. Kisner, Mehdi Rezaie, John Moustakas, Claire Poppett, Hu Zou, T. Claybaugh, P. Doel, B. A. Weaver, M. Schubnell, Adam D. Myers, Francisco Prada, William Davison
article en

Abstract

Abstract 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 r lap score as a trustworthiness cut, making for a more robust and accurate supernova classifier than before.

Publications of the Astronomical Society of the PacificVol. 137(9)
Laboratoire de Physique Nucléaire et de Hautes Énergies (FR)
National Science Foundation, U.S. Department of Energy, Gordon and Betty Moore Foundation, Ministerio de Ciencia e Innovación, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Office of Science, Science and Technology Facilities Council, Division of Astronomical Sciences
Openalex Percentile: Top 100%
Gamma-ray bursts and supernovae
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