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From particle identification to the discovery of the Higgs boson, deeplearning algorithms have become an increasingly important tool for dataanalysis at the Large Hadron Collider (LHC). We present an innovativeend-to-end deep learning approach for jet identification at the Compact MuonSolenoid (CMS) experiment at the LHC. The method combines deep neural networkswith low-level detector information, such as calorimeter energy deposits andtracking information, to build a discriminator to identify different particlespecies. Using two physics examples as references: electron vs. photondiscrimination and quark vs. gluon discrimination, we demonstrate theperformance of the end-to-end approach on simulated events with full detectorgeometry as available in the CMS Open Data. We also offer insights into theimportance of the information extracted from various sub-detectors and describehow end-to-end techniques can be extended to event-level classification usinginformation from the whole CMS detector.

End-to-end particle and event identification at the Large Hadron Collider with CMS Open Data