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

TL;DR

This paper develops novel deep learning classifiers that analyze raw detector data to directly distinguish Higgs boson decays from background events at the LHC, demonstrating improved performance over traditional methods.

Contribution

It introduces end-to-end image-based classifiers that utilize low-level detector data for particle collision event classification, advancing beyond traditional kinematic analysis.

Findings

Classifiers successfully learn from electromagnetic shower shapes and energies.

End-to-end classifiers outperform kinematic-based methods in unresolved particle cases.

Demonstrates feasibility of direct raw data analysis for event classification at LHC.

Abstract

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.