  We present a novel method of classifying Type Ia supernovae using
convolutional neural networks, a neural network framework typically used for
image recognition. Our model is trained on photometric information only,
eliminating the need for accurate redshift data. Photometric data is
pre-processed via 2D Gaussian process regression into two-dimensional images
created from flux values at each location in wavelength-time space. These "flux
heatmaps" of each supernova detection, along with "uncertainty heatmaps" of the
Gaussian process uncertainty, constitute the dataset for our model. This
preprocessing step not only smooths over irregular sampling rates between
filters but also allows SCONE to be independent of the filter set on which it
was trained. Our model has achieved impressive performance without redshift on
the in-distribution SNIa classification problem: $99.73 \pm 0.26$% test
accuracy with no over/underfitting on a subset of supernovae from PLAsTiCC's
unblinded test dataset. We have also achieved $98.18 \pm 0.3$% test accuracy
performing 6-way classification of supernovae by type. The out-of-distribution
performance does not fully match the in-distribution results, suggesting that
the detailed characteristics of the training sample in comparison to the test
sample have a big impact on the performance. We discuss the implication and
directions for future work. 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.
