  Particle detectors record the interactions of subatomic particles and their
passage through matter. The identification of these particles is necessary for
in-depth physics analysis. While particles can be identified by their
individual behavior as they travel through matter, the full context of the
interaction in which they are produced can aid the classification task
substantially. We have developed the first convolutional neural network for
particle identification which uses context information. This is also the first
implementation of a four-tower siamese-type architecture both for separation of
independent inputs and inclusion of context information. The network classifies
clusters of energy deposits from the NOvA neutrino detectors as electrons,
muons, photons, pions, and protons with an overall efficiency and purity of
83.3% and 83.5%, respectively. We show that providing the network with context
information improves performance by comparing our results with a network
trained without context information.
