Statistical Classification Techniques for Photometric Supernova Typing

TL;DR

This paper evaluates boosting and kernel density estimation techniques for classifying supernovae using lightcurves alone, showing they perform comparably to existing methods without needing redshift information.

Contribution

It introduces and compares boosting and kernel density estimation methods for supernova classification, emphasizing minimal astrophysical input and the importance of representative training samples.

Findings

Methods perform comparably to template fitting techniques.

They do not require host galaxy redshift information.

Performance depends on the representativeness of training samples.

Abstract

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.