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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-11-01
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| Series: | Journal of High Energy Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/JHEP11(2021)219 |
| _version_ | 1818721612388630528 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-17T20:41:30Z |
| format | Article |
| id | doaj.art-896cea2d79134c30b19fa6e5bb18ae18 |
| institution | Directory Open Access Journal |
| issn | 1029-8479 |
| language | English |
| last_indexed | 2024-12-17T20:41:30Z |
| publishDate | 2021-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of High Energy Physics |
| spelling | doaj.art-896cea2d79134c30b19fa6e5bb18ae182022-12-21T21:33:17ZengSpringerOpenJournal of High Energy Physics1029-84792021-11-0120211113210.1007/JHEP11(2021)219Deep Learning for the classification of quenched jetsL. Apolinário0N. F. Castro1M. Crispim Romão2J. G. Milhano3R. Pedro4F. C. R. Peres5Laboratório de Instrumentação e Física Experimental de Partículas (LIP)Laboratório de Instrumentação e Física Experimental de Partículas (LIP)Laboratório de Instrumentação e Física Experimental de Partículas (LIP)Laboratório de Instrumentação e Física Experimental de Partículas (LIP)Laboratório de Instrumentação e Física Experimental de Partículas (LIP)Laboratório de Instrumentação e Física Experimental de Partículas (LIP)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.https://doi.org/10.1007/JHEP11(2021)219Heavy Ion PhenomenologyJets |
| spellingShingle | 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 Journal of High Energy Physics Heavy Ion Phenomenology Jets |
| title | 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_short | Deep Learning for the classification of quenched jets |
| title_sort | deep learning for the classification of quenched jets |
| topic | Heavy Ion Phenomenology Jets |
| url | https://doi.org/10.1007/JHEP11(2021)219 |
| work_keys_str_mv | AT lapolinario deeplearningfortheclassificationofquenchedjets AT nfcastro deeplearningfortheclassificationofquenchedjets AT mcrispimromao deeplearningfortheclassificationofquenchedjets AT jgmilhano deeplearningfortheclassificationofquenchedjets AT rpedro deeplearningfortheclassificationofquenchedjets AT fcrperes deeplearningfortheclassificationofquenchedjets |