A statistical approach to identify superluminous supernovae and probe their diversity [HEAP]


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 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 describes and defines a main population of SLSNe I, and presents a method for identifying SLSNe I in current and future transient surveys. 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.

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C. Inserra, S. Prajs, C. Gutierrez, et. al.
Mon, 13 Nov 17

Comments: 13 pages, 7 figures, submitted to ApJ