Statistical Classification Techniques for Photometric Supernova Typing
  Future photometric supernova surveys will produce vastly more candidates than
can be followed up spectroscopically, highlighting the need for effective
classification methods based on lightcurves alone. Here we introduce boosting
and kernel density estimation techniques which have minimal astrophysical
input, and compare their performance on 20,000 simulated Dark Energy Survey
lightcurves. We demonstrate that these methods are comparable to the best
template fitting methods currently used, and in particular do not require the
redshift of the host galaxy or candidate. However both methods require a
training sample that is representative of the full population, so typical
spectroscopic supernova subsamples will lead to poor performance. To enable the
full potential of such blind methods, we recommend that representative training
samples should be used and so specific attention should be given to their
creation in the design phase of future photometric surveys.
