In:
The Astrophysical Journal, American Astronomical Society, Vol. 937, No. 1 ( 2022-09-01), p. 13-
Kurzfassung:
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 our SuperRAENN and Superphot algorithms. 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 sample in the LSST era through photometric selection and study.
Materialart:
Online-Ressource
ISSN:
0004-637X
,
1538-4357
DOI:
10.3847/1538-4357/ac87ff
Sprache:
Unbekannt
Verlag:
American Astronomical Society
Publikationsdatum:
2022
ZDB Id:
2207648-7
ZDB Id:
1473835-1
SSG:
16,12
