A statistical approach to identify superluminous supernovae and probe their diversity

TL;DR

This paper develops a photometric method to identify hydrogen-poor superluminous supernovae (SLSNe I), revealing their diversity and proposing a new classification based on light curve and spectroscopic features, with implications for cosmology.

Contribution

It introduces a novel photometric parameter space for SLSNe I, enabling identification without arbitrary thresholds and uncovering two distinct subclasses with different physical properties.

Findings

90% of known SLSNe I fit the new definition.

Identification of two subclasses: 'Fast' and 'Slow' SLSNe.

Photometric and spectroscopic analysis supports subclass distinction.

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 analyse existing and future transient datasets. 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.