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Quantitative Comparison of Deep Neural Networks for Quark/Gluon Jet Discrimination

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Title: Quantitative Comparison of Deep Neural Networks for Quark/Gluon Jet Discrimination
Author(s): Kallonen, Kimmo
Contributor: University of Helsinki, Faculty of Science, Fysiikan laitos
Discipline: Teoreettinen fysiikka
Language: English
Acceptance year: 2019
Quarks and gluons are elementary particles called partons, which produce collimated sprays of particles when protons are collided head-on at the Large Hadron Collider. These observable signatures of the quarks and gluons are called jets and are recorded by huge particle detectors, such as the Compact Muon Solenoid. The reconstruction of the jets from detector signals attempts to trace the particle-level information all the way back to the level of the initial collision event with the initating partons. Jets originating from gluons and the three lightest quarks are very similar to each other, only exhibiting subtle differences caused by the fact that gluons radiate more intensely. Quark/gluon jet discrimination algorithms are dedicated to identifying these two types of jets. Traditionally, likelihood-based quark/gluon discriminators have been used. While machine learning is nothing new to the high energy physics community, the advent of deep neural networks caused an upheaval and they are now being implemented to take on various tasks across the research field, including quark/gluon discrimination. In this thesis, three different deep neural network models are presented and their comparative performance in quark/gluon discrimination is evaluated in seven different bins of varying jet transverse momentum and pseudorapidity. The performance of a likelihood-based discriminator is used as a benchmark. Deep neural networks prove to provide excellent performance in quark/gluon discrimination, with a jet image-based visual recognition model being the most robust and offering the largest performance improvement over the benchmark discriminator.

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