We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique uses low-level detector representation of high-energy collision event as inputs to deep learning algorithms. In this study, we use low-level detector information from the simulated Compact Muon Solenoid (CMS) open data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves an area under the receiver operator curve (AUC) score of $0.975\pm 0.002$ for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier AUC score increases to $0.9824\pm 0.0013$, serving as the first performance benchmark for these CMS open data samples.

• Received 13 July 2021
• Accepted 6 January 2022

DOI:https://doi.org/10.1103/PhysRevD.105.052008