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Title: Photometrically Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine-learning-based Classification
Abstract

With the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), it is expected that only ∼0.1% of all transients will be classified spectroscopically. To conduct studies of rare transients, such as Type I superluminous supernovae (SLSNe), we must instead rely on photometric classification. In this vein, here we carry out a pilot study of SLSNe from the Pan-STARRS1 Medium Deep Survey (PS1-MDS), classified photometrically with ourSuperRAENNandSuperphotalgorithms. We first construct a subsample of the photometric sample using a list of simple selection metrics designed to minimize contamination and ensure sufficient data quality for modeling. We then fit the multiband light curves with a magnetar spin-down model using the Modular Open-Source Fitter for Transients (MOSFiT). Comparing the magnetar engine and ejecta parameter distributions of the photometric sample to those of the PS1-MDS spectroscopic sample and a larger literature spectroscopic sample, we find that these samples are consistent overall, but that the photometric sample extends to slower spins and lower ejecta masses, which correspond to lower-luminosity events, as expected for photometric selection. While our PS1-MDS photometric sample is still smaller than the overall SLSN spectroscopic sample, our methodology paves the way for an orders-of-magnitude increase in the SLSN more » sample in the LSST era through photometric selection and study.

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Authors:
; ; ;
Award ID(s):
2019786
Publication Date:
NSF-PAR ID:
10371404
Journal Name:
The Astrophysical Journal
Volume:
937
Issue:
1
Page Range or eLocation-ID:
Article No. 13
ISSN:
0004-637X
Publisher:
DOI PREFIX: 10.3847
Sponsoring Org:
National Science Foundation
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