  This paper describes the construction of novel end-to-end image-based
classifiers that directly leverage low-level simulated detector data to
discriminate signal and background processes in pp collision events at the
Large Hadron Collider at CERN. To better understand what end-to-end classifiers
are capable of learning from the data and to address a number of associated
challenges, we distinguish the decay of the standard model Higgs boson into two
photons from its leading background sources using high-fidelity simulated CMS
Open Data. We demonstrate the ability of end-to-end classifiers to learn from
the angular distribution of the photons recorded as electromagnetic showers,
their intrinsic shapes, and the energy of their constituent hits, even when the
underlying particles are not fully resolved, delivering a clear advantage in
such cases over purely kinematics-based classifiers.
