  There has been considerable recent activity applying deep convolutional
neural nets (CNNs) to data from particle physics experiments. Current
approaches on ATLAS/CMS have largely focussed on a subset of the calorimeter,
and for identifying objects or particular particle types. We explore approaches
that use the entire calorimeter, combined with track information, for directly
conducting physics analyses: i.e. classifying events as known-physics
background or new-physics signals.
  We use an existing RPV-Supersymmetry analysis as a case study and explore
CNNs on multi-channel, high-resolution sparse images: applied on GPU and
multi-node CPU architectures (including Knights Landing (KNL) Xeon Phi nodes)
on the Cori supercomputer at NERSC.
