End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data
- Resource Type
- Working Paper
- Authors
- Andrews, Michael; Alison, John; An, Sitong; Bryant, Patrick; Burkle, Bjorn; Gleyzer, Sergei; Narain, Meenakshi; Paulini, Manfred; Poczos, Barnabas; Usai, Emanuele
- Source
- Nucl. Instrum. Methods Phys. Res. A 977, 164304 (2020)
- Subject
- High Energy Physics - Experiment
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Machine Learning
Physics - Data Analysis, Statistics and Probability
- Language
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.
Comment: 10 pages, 5 figures, 7 tables; v2: published version