  Host galaxy identification is a crucial step for modern supernova (SN)
surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey
Telescope (LSST), which will discover SNe by the thousands. Spectroscopic
resources are limited, so in the absence of real-time SN spectra these surveys
must rely on host galaxy spectra to obtain accurate redshifts for the Hubble
diagram and to improve photometric classification of SNe. In addition, SN
luminosities are known to correlate with host-galaxy properties. Therefore,
reliable identification of host galaxies is essential for cosmology and SN
science. We simulate SN events and their locations within their host galaxies
to develop and test methods for matching SNe to their hosts. We use both real
and simulated galaxy catalog data from the Advanced Camera for Surveys General
Catalog and MICECATv2.0, respectively. We also incorporate "hostless" SNe
residing in undetected faint hosts into our analysis, with an assumed hostless
rate of 5%. Our fully automated algorithm is run on catalog data and matches
SNe to their hosts with 91% accuracy. We find that including a machine learning
component, run after the initial matching algorithm, improves the accuracy
(purity) of the matching to 97% with a 2% cost in efficiency (true positive
rate). Although the exact results are dependent on the details of the survey
and the galaxy catalogs used, the method of identifying host galaxies we
outline here can be applied to any transient survey.
