A statistical approach to identify superluminous supernovae and probe their diversity

Inserra, C. ORCID: https://orcid.org/0000-0002-3968-4409, Prajs, S., Gutierrez, C. P., Angus, C., Smith, M. and Sullivan, M. 2018. A statistical approach to identify superluminous supernovae and probe their diversity. Astrophysical Journal 854 (2) , p. 175. 10.3847/1538-4357/aaaaaa

Preview

PDF - Published Version Download (1MB) | Preview

Abstract

We investigate the identification of hydrogen-poor superluminous supernovae (SLSNe I) using a photometric analysis, without including an arbitrary magnitude threshold. We assemble a homogeneous sample of previously classified SLSNe I from the literature, and fit their light curves using Gaussian processes. From the fits, we identify four photometric parameters that have a high statistical significance when correlated, and combine them in a parameter space that conveys information on their luminosity and color evolution. This parameter space presents a new definition for SLSNe I, which can be used to analyze existing and future transient data sets. We find that 90% of previously classified SLSNe I meet our new definition. We also examine the evidence for two subclasses of SLSNe I, combining their photometric evolution with spectroscopic information, namely the photospheric velocity and its gradient. A cluster analysis reveals the presence of two distinct groups. "Fast" SLSNe show fast light curves and color evolution, large velocities, and a large velocity gradient. "Slow" SLSNe show slow light curve and color evolution, small expansion velocities, and an almost non-existent velocity gradient. Finally, we discuss the impact of our analyses in the understanding of the powering engine of SLSNe, and their implementation as cosmological probes in current and future surveys.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Physics and Astronomy
Publisher:	American Astronomical Society
ISSN:	0004-637X
Date of First Compliant Deposit:	20 December 2018
Date of Acceptance:	23 January 2018
Last Modified:	06 May 2023 03:40
URI:	https://orca.cardiff.ac.uk/id/eprint/117855

Citation Data

Cited 30 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item