  We report results from the Supernova Photometric Classification Challenge
(SNPCC), a publicly released mix of simulated supernovae (SNe), with types (Ia,
Ibc, and II) selected in proportion to their expected rate. The simulation was
realized in the griz filters of the Dark Energy Survey (DES) with realistic
observing conditions (sky noise, point-spread function and atmospheric
transparency) based on years of recorded conditions at the DES site.
Simulations of non-Ia type SNe are based on spectroscopically confirmed light
curves that include unpublished non-Ia samples donated from the Carnegie
Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan
Digital Sky Survey-II (SDSS-II). A spectroscopically confirmed subset was
provided for training. We challenged scientists to run their classification
algorithms and report a type and photo-z for each SN. Participants from 10
groups contributed 13 entries for the sample that included a host-galaxy
photo-z for each SN, and 9 entries for the sample that had no redshift
information. Several different classification strategies resulted in similar
performance, and for all entries the performance was significantly better for
the training subset than for the unconfirmed sample. For the spectroscopically
unconfirmed subset, the entry with the highest average figure of merit for
classifying SNe~Ia has an efficiency of 0.96 and an SN~Ia purity of 0.79. As a
public resource for the future development of photometric SN classification and
photo-z estimators, we have released updated simulations with improvements
based on our experience from the SNPCC, added samples corresponding to the
Large Synoptic Survey Telescope (LSST) and the SDSS, and provided the answer
keys so that developers can evaluate their own analysis.
