Photometrically-Classified Superluminous Supernovae from the Pan-STARRS1
Medium Deep Survey: A Case Study for Science with Machine Learning-Based
Classification
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 sub-sample 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 multi-band 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 overall consistent, 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 to an orders-of-magnitude increase in the
SLSN sample in the LSST era through photometric selection and study.Comment: 13 pages, 6 figures, submitted to Ap
