Fuzzy Supernova Templates I: Classification
  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% accuracy. In addition, the SOFT
method has the potential to classify supernovae into sub-types, providing
photometric identification of very rare or peculiar explosions. The accuracy
and precision of the SOFT method is verified using Monte Carlo simulations as
well as real SN light curves from the Sloan Digital Sky Survey and the
SuperNova Legacy Survey. In a subsequent paper the SOFT method is extended to
address the problem of parameter estimation, providing estimates of redshift,
distance, and host galaxy extinction without any spectroscopy.
