CERN Accelerating science

Published Articles
Report number	arXiv:1902.08276
Title	End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data
Author(s)	Andrews, M. (Carnegie Mellon U.) ; Alison, J. (Carnegie Mellon U.) ; An, S. (Carnegie Mellon U. ; CERN) ; Bryant, Patrick ; Burkle, B. (Brown U.) ; Gleyzer, S. (Alabama U.) ; Narain, M. (Brown U.) ; Paulini, M. (Carnegie Mellon U.) ; Poczos, B. (Carnegie Mellon U.) ; Usai, E. (Brown U.)
Publication	2020-10-11
Imprint	2019-02-21
Number of pages	8
Note	10 pages, 5 figures, 7 tables; v2: published version
In:	Nucl. Instrum. Methods Phys. Res., A 977 (2020) 164304
DOI	10.1016/j.nima.2020.164304
Subject category	physics.data-an ; Other Fields of Physics ; cs.LG ; Computing and Computers ; cs.CV ; Computing and Computers ; hep-ex ; Particle Physics - Experiment
Accelerator/Facility, Experiment	CERN LHC ; CMS
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.
Copyright/License	preprint: (License: arXiv nonexclusive-distrib 1.0) publication: © 2020 The Authors (License: CC-BY-4.0)

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