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

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End-to-End Physics Event Classification with CMS Open Data: Applying  Image-Based Deep Learning to Detector Data for the Direct Classification of  Collision Events at the LHC