Deep Learning for the classification of quenched jets

Abstract An important aspect of the study of Quark-Gluon Plasma (QGP) in ultrarelativistic collisions of heavy ions is the ability to identify, in experimental data, a subset of the jets that were strongly modified by the interaction with the QGP. In this work, we propose studying Deep Learning tech...

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Autores principales:	L. Apolinário, N. F. Castro, M. Crispim Romão, J. G. Milhano, R. Pedro, F. C. R. Peres
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Heavy Ion Phenomenology Jets Nuclear and particle physics. Atomic energy. Radioactivity QC770-798
Acceso en línea:	https://doaj.org/article/896cea2d79134c30b19fa6e5bb18ae18
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spelling	oai:doaj.org-article:896cea2d79134c30b19fa6e5bb18ae182021-12-05T12:25:13ZDeep Learning for the classification of quenched jets10.1007/JHEP11(2021)2191029-8479https://doaj.org/article/896cea2d79134c30b19fa6e5bb18ae182021-11-01T00:00:00Zhttps://doi.org/10.1007/JHEP11(2021)219https://doaj.org/toc/1029-8479Abstract An important aspect of the study of Quark-Gluon Plasma (QGP) in ultrarelativistic collisions of heavy ions is the ability to identify, in experimental data, a subset of the jets that were strongly modified by the interaction with the QGP. In this work, we propose studying Deep Learning techniques for this purpose. Samples of Z+jet events were simulated in vacuum (pp collisions) and medium (PbPb collisions) and used to train Deep Neural Networks with the objective of discriminating between medium- and vacuum-like jets within the medium (PbPb) sample. Dedicated Convolutional Neural Networks, Dense Neural Networks and Recurrent Neural Networks were developed and trained, and their performance was studied. Our results show the potential of these techniques for the identification of jet quenching effects induced by the presence of the QGP.L. ApolinárioN. F. CastroM. Crispim RomãoJ. G. MilhanoR. PedroF. C. R. PeresSpringerOpenarticleHeavy Ion PhenomenologyJetsNuclear and particle physics. Atomic energy. RadioactivityQC770-798ENJournal of High Energy Physics, Vol 2021, Iss 11, Pp 1-32 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Heavy Ion Phenomenology Jets Nuclear and particle physics. Atomic energy. Radioactivity QC770-798
spellingShingle	Heavy Ion Phenomenology Jets Nuclear and particle physics. Atomic energy. Radioactivity QC770-798 L. Apolinário N. F. Castro M. Crispim Romão J. G. Milhano R. Pedro F. C. R. Peres Deep Learning for the classification of quenched jets
description	Abstract An important aspect of the study of Quark-Gluon Plasma (QGP) in ultrarelativistic collisions of heavy ions is the ability to identify, in experimental data, a subset of the jets that were strongly modified by the interaction with the QGP. In this work, we propose studying Deep Learning techniques for this purpose. Samples of Z+jet events were simulated in vacuum (pp collisions) and medium (PbPb collisions) and used to train Deep Neural Networks with the objective of discriminating between medium- and vacuum-like jets within the medium (PbPb) sample. Dedicated Convolutional Neural Networks, Dense Neural Networks and Recurrent Neural Networks were developed and trained, and their performance was studied. Our results show the potential of these techniques for the identification of jet quenching effects induced by the presence of the QGP.
format	article
author	L. Apolinário N. F. Castro M. Crispim Romão J. G. Milhano R. Pedro F. C. R. Peres
author_facet	L. Apolinário N. F. Castro M. Crispim Romão J. G. Milhano R. Pedro F. C. R. Peres
author_sort	L. Apolinário
title	Deep Learning for the classification of quenched jets
title_short	Deep Learning for the classification of quenched jets
title_full	Deep Learning for the classification of quenched jets
title_fullStr	Deep Learning for the classification of quenched jets
title_full_unstemmed	Deep Learning for the classification of quenched jets
title_sort	deep learning for the classification of quenched jets
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/896cea2d79134c30b19fa6e5bb18ae18
work_keys_str_mv	AT lapolinario deeplearningfortheclassificationofquenchedjets AT nfcastro deeplearningfortheclassificationofquenchedjets AT mcrispimromao deeplearningfortheclassificationofquenchedjets AT jgmilhano deeplearningfortheclassificationofquenchedjets AT rpedro deeplearningfortheclassificationofquenchedjets AT fcrperes deeplearningfortheclassificationofquenchedjets
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Deep Learning for the classification of quenched jets

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