Identifying quenched jets in heavy ion collisions with machine learning
Abstract Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modified by the interaction with the quark-gluon plasma. Modifications of the hard substructure of jets can be explored with modern data-driven techniques. In this study, a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-04-01
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| Series: | Journal of High Energy Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/JHEP04(2023)140 |
| _version_ | 1827890627532029952 |
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| author | Lihan Liu Julia Velkovska Yilun Wu Marta Verweij |
| author_facet | Lihan Liu Julia Velkovska Yilun Wu Marta Verweij |
| author_sort | Lihan Liu |
| collection | DOAJ |
| description | Abstract Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modified by the interaction with the quark-gluon plasma. Modifications of the hard substructure of jets can be explored with modern data-driven techniques. In this study, a machine learning approach to the identification of quenched jets is designed. Jet showering processes are simulated with a jet quenching model Jewel and a non-quenching model Pythia 8. Sequential substructure variables are extracted from the jet clustering history following an angular-ordered sequence and are used in the training of a neural network built on top of a long short-term memory network. We show that this approach successfully identifies the quenching effect in the presence of the large uncorrelated background of soft particles created in heavy-ion collisions. |
| first_indexed | 2024-03-12T21:12:23Z |
| format | Article |
| id | doaj.art-debc5dc2ec834be2ac060fcd618812d6 |
| institution | Directory Open Access Journal |
| issn | 1029-8479 |
| language | English |
| last_indexed | 2024-03-12T21:12:23Z |
| publishDate | 2023-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of High Energy Physics |
| spelling | doaj.art-debc5dc2ec834be2ac060fcd618812d62023-07-30T11:05:03ZengSpringerOpenJournal of High Energy Physics1029-84792023-04-012023412310.1007/JHEP04(2023)140Identifying quenched jets in heavy ion collisions with machine learningLihan Liu0Julia Velkovska1Yilun Wu2Marta Verweij3Department of Physics and Astronomy, Vanderbilt UniversityDepartment of Physics and Astronomy, Vanderbilt UniversityDepartment of Physics and Astronomy, Vanderbilt UniversityDepartment of Physics, Utrecht UniversityAbstract Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modified by the interaction with the quark-gluon plasma. Modifications of the hard substructure of jets can be explored with modern data-driven techniques. In this study, a machine learning approach to the identification of quenched jets is designed. Jet showering processes are simulated with a jet quenching model Jewel and a non-quenching model Pythia 8. Sequential substructure variables are extracted from the jet clustering history following an angular-ordered sequence and are used in the training of a neural network built on top of a long short-term memory network. We show that this approach successfully identifies the quenching effect in the presence of the large uncorrelated background of soft particles created in heavy-ion collisions.https://doi.org/10.1007/JHEP04(2023)140Jets and Jet SubstructureQuark-Gluon Plasma |
| spellingShingle | Lihan Liu Julia Velkovska Yilun Wu Marta Verweij Identifying quenched jets in heavy ion collisions with machine learning Journal of High Energy Physics Jets and Jet Substructure Quark-Gluon Plasma |
| title | Identifying quenched jets in heavy ion collisions with machine learning |
| title_full | Identifying quenched jets in heavy ion collisions with machine learning |
| title_fullStr | Identifying quenched jets in heavy ion collisions with machine learning |
| title_full_unstemmed | Identifying quenched jets in heavy ion collisions with machine learning |
| title_short | Identifying quenched jets in heavy ion collisions with machine learning |
| title_sort | identifying quenched jets in heavy ion collisions with machine learning |
| topic | Jets and Jet Substructure Quark-Gluon Plasma |
| url | https://doi.org/10.1007/JHEP04(2023)140 |
| work_keys_str_mv | AT lihanliu identifyingquenchedjetsinheavyioncollisionswithmachinelearning AT juliavelkovska identifyingquenchedjetsinheavyioncollisionswithmachinelearning AT yilunwu identifyingquenchedjetsinheavyioncollisionswithmachinelearning AT martaverweij identifyingquenchedjetsinheavyioncollisionswithmachinelearning |