  At the extreme energies of the Large Hadron Collider, massive particles can
be produced at such high velocities that their hadronic decays are collimated
and the resulting jets overlap. Deducing whether the substructure of an
observed jet is due to a low-mass single particle or due to multiple decay
objects of a massive particle is an important problem in the analysis of
collider data. Traditional approaches have relied on expert features designed
to detect energy deposition patterns in the calorimeter, but the complexity of
the data make this task an excellent candidate for the application of machine
learning tools. The data collected by the detector can be treated as a
two-dimensional image, lending itself to the natural application of image
classification techniques. In this work, we apply deep neural networks with a
mixture of locally-connected and fully-connected nodes. Our experiments
demonstrate that without the aid of expert features, such networks match or
modestly outperform the current state-of-the-art approach for discriminating
between jets from single hadronic particles and overlapping jets from pairs of
collimated hadronic particles, and that such performance gains persist in the
presence of pileup interactions.
