Fuzzy Supernova Templates I: Classification
Steven A. Rodney, John L. Tonry

TL;DR
This paper introduces a fuzzy set theory-based method called SOFT for classifying supernovae using only photometric light curves, achieving high accuracy and enabling identification of rare or peculiar types.
Contribution
It presents the SOFT method, an innovative fuzzy template approach that improves supernova classification accuracy over previous Bayesian methods using photometric data.
Findings
SOFT correctly classifies 98% of well-sampled SNe.
It can distinguish Type Ia from core-collapse SNe.
Potential to classify supernova sub-types and peculiar explosions.
Abstract
Modern supernova (SN) surveys are now uncovering stellar explosions at rates that far surpass what the world's spectroscopic resources can handle. In order to make full use of these SN datasets, it is necessary to use analysis methods that depend only on the survey photometry. This paper presents two methods for utilizing a set of SN light curve templates to classify SN objects. In the first case we present an updated version of the Bayesian Adaptive Template Matching program (BATM). To address some shortcomings of that strictly Bayesian approach, we introduce a method for Supernova Ontology with Fuzzy Templates (SOFT), which utilizes Fuzzy Set Theory for the definition and combination of SN light curve models. For well-sampled light curves with a modest signal to noise ratio (S/N>10), the SOFT method can correctly separate thermonuclear (Type Ia) SNe from core collapse SNe with 98%…
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