End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data
This work addresses jet classification in high-energy physics, which is incremental as it applies an end-to-end approach to a known domain-specific task.
The paper tackled the problem of classifying quark- vs. gluon-initiated jets using end-to-end classifiers on simulated low-level detector data, achieving competitive performance compared to existing state-of-the-art methods and demonstrating robustness against underlying event and pile-up effects.
We describe the construction of end-to-end jet image classifiers based on simulated low-level detector data to discriminate quark- vs. gluon-initiated jets with high-fidelity simulated CMS Open Data. We highlight the importance of precise spatial information and demonstrate competitive performance to existing state-of-the-art jet classifiers. We further generalize the end-to-end approach to event-level classification of quark vs. gluon di-jet QCD events. We compare the fully end-to-end approach to using hand-engineered features and demonstrate that the end-to-end algorithm is robust against the effects of underlying event and pile-up.