  Automated photometric supernova classification has become an active area of
research in recent years in light of current and upcoming imaging surveys such
as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given
that spectroscopic confirmation of type for all supernovae discovered will be
impossible. Here, we develop a multi-faceted classification pipeline, combining
existing and new approaches. Our pipeline consists of two stages: extracting
descriptive features from the light curves and classification using a machine
learning algorithm. Our feature extraction methods vary from model-dependent
techniques, namely SALT2 fits, to more independent techniques fitting
parametric models to curves, to a completely model-independent wavelet
approach. We cover a range of representative machine learning algorithms,
including naive Bayes, k-nearest neighbors, support vector machines, artificial
neural networks and boosted decision trees (BDTs). We test the pipeline on
simulated multi-band DES light curves from the Supernova Photometric
Classification Challenge. Using the commonly used area under the curve (AUC) of
the Receiver Operating Characteristic as a metric, we find that the SALT2 fits
and the wavelet approach, with the BDTs algorithm, each achieves an AUC of
0.98, where 1 represents perfect classification. We find that a representative
training set is essential for good classification, whatever the feature set or
algorithm, with implications for spectroscopic follow-up. Importantly, we find
that by using either the SALT2 or the wavelet feature sets with a BDT
algorithm, accurate classification is possible purely from light curve data,
without the need for any redshift information.
