End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data

Michael Andrews; John Alison; Sitong An; Patrick Bryant; Bjorn Burkle; Sergei Gleyzer; Meenakshi Narain; Manfred Paulini; Barnabas Poczos; Emanuele Usai

doi:10.1016/j.nima.2020.164304

arXiv · 1902.08276

End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data

Michael Andrews·John Alison·Sitong An·Patrick Bryant·Bjorn Burkle·Sergei Gleyzer·Meenakshi Narain·Manfred Paulini·Barnabas Poczos·Emanuele Usai

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Abstract

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.

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hep-ex cs.CV cs.LG physics.data-an

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Michael Andrews, John Alison, Sitong An, Patrick Bryant, Bjorn Burkle, Sergei Gleyzer, Meenakshi Narain, Manfred Paulini, Barnabas Poczos, Emanuele Usai. 2019-02-21. End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data. https://doi.org/10.1016/j.nima.2020.164304

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