Classifying Supernovae Using Only Galaxy Data
  We present a new method for probabilistically classifying supernovae (SNe)
without using SN spectral or photometric data. Unlike all previous studies to
classify SNe without spectra, this technique does not use any SN photometry.
Instead, the method relies on host-galaxy data. We build upon the well-known
correlations between SN classes and host-galaxy properties, specifically that
core-collapse SNe rarely occur in red, luminous, or early-type galaxies. Using
the nearly spectroscopically complete Lick Observatory Supernova Search sample
of SNe, we determine SN fractions as a function of host-galaxy properties.
Using these data as inputs, we construct a Bayesian method for determining the
probability that a SN is of a particular class. This method improves a common
classification figure of merit by a factor of >2, comparable to the best
light-curve classification techniques. Of the galaxy properties examined,
morphology provides the most discriminating information. We further validate
this method using SN samples from the Sloan Digital Sky Survey and the Palomar
Transient Factory. We demonstrate that this method has wide-ranging
applications, including separating different subclasses of SNe and determining
the probability that a SN is of a particular class before photometry or even
spectra can. Since this method uses completely independent data from
light-curve techniques, there is potential to further improve the overall
purity and completeness of SN samples and to test systematic biases of the
light-curve techniques. Further enhancements to the host-galaxy method,
including additional host-galaxy properties, combination with light-curve
methods, and hybrid methods should further improve the quality of SN samples
from past, current, and future transient surveys.
