Title: A statistical approach to identify superluminous supernovae and probe their diversity,
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.