A1 Refereed original research article in a scientific journal
A Statistical Approach to Identify Superluminous Supernovae and Probe Their Diversity
Authors: Inserra C, Prajs S, Gutierrez CP, Angus C, Smith M, Sullivan M
Publisher: IOP PUBLISHING LTD
Publication year: 2018
Journal: Astrophysical Journal
Journal name in source: ASTROPHYSICAL JOURNAL
Journal acronym: ASTROPHYS J
Article number: ARTN 175
Volume: 854
Issue: 2
Number of pages: 12
ISSN: 0004-637X
eISSN: 1538-4357
DOI: https://doi.org/10.3847/1538-4357/aaaaaa
Web address : https://iopscience.iop.org/article/10.3847/1538-4357/aaaaaa
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.
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.
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