Photometric Classification of Early-Time Supernova Lightcurves with
  SCONE
  In this work, we present classification results on early supernova
lightcurves from SCONE, a photometric classifier that uses convolutional neural
networks to categorize supernovae (SNe) by type using lightcurve data. SCONE is
able to identify SN types from lightcurves at any stage, from the night of
initial alert to the end of their lifetimes. Simulated LSST SNe lightcurves
were truncated at 0, 5, 15, 25, and 50 days after the trigger date and used to
train Gaussian processes in wavelength and time space to produce
wavelength-time heatmaps. SCONE uses these heatmaps to perform 6-way
classification between SN types Ia, II, Ibc, Ia-91bg, Iax, and SLSN-I. SCONE is
able to perform classification with or without redshift, but we show that
incorporating redshift information improves performance at each epoch. SCONE
achieved 75% overall accuracy at the date of trigger (60% without redshift),
and 89% accuracy 50 days after trigger (82% without redshift). SCONE was also
tested on bright subsets of SNe (r<20 mag) and produced 91% accuracy at the
date of trigger (83% without redshift) and 95% 5 days after trigger (94.7%
without redshift). SCONE is the first application of convolutional neural
networks to the early-time photometric transient classification problem. All of
the data processing and model code developed for this paper can be found in the
SCONE software package located at github.com/helenqu/scone.
