  The complex events observed at the NOvA long-baseline neutrino oscillation
experiment contain vital information for understanding the most elusive
particles in the standard model. The NOvA detectors observe interactions of
neutrinos from the NuMI beam at Fermilab. Associating the particles produced in
these interaction events to their source particles, a process known as
reconstruction, is critical for accurately measuring key parameters of the
standard model. Events may contain several particles, each producing sparse
high-dimensional spatial observations, and current methods are limited to
evaluating individual particles. To accurately label these numerous,
high-dimensional observations, we present a novel neural network architecture
that combines the spatial learning enabled by convolutions with the contextual
learning enabled by attention. This joint approach, TransformerCVN,
simultaneously classifies each event and reconstructs every individual
particle's identity. TransformerCVN classifies events with 90\% accuracy and
improves the reconstruction of individual particles by 6\% over baseline
methods which lack the integrated architecture of TransformerCVN. In addition,
this architecture enables us to perform several interpretability studies which
provide insights into the network's predictions and show that TransformerCVN
discovers several fundamental principles that stem from the standard model.
