Jet substructure observables for jet quenching in Quark Gluon Plasma: a Machine Learning driven analysis
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866929218721415168 |
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| author | Romão, Miguel Crispim Milhano, José Guilherme van Leeuwen, Marco |
| author_facet | Romão, Miguel Crispim Milhano, José Guilherme van Leeuwen, Marco |
| contents | We present a survey of a comprehensive set of jet substructure observables commonly used to study the modifications of jets resulting from interactions with the Quark Gluon Plasma in Heavy Ion Collisions. The \jewel{} event generator is used to produce simulated samples of quenched and unquenched jets. Three distinct analyses using Machine Learning techniques on the jet substructure observables have been performed to identify both linear and non-linear relations between the observables, and to distinguish the Quenched and Unquenched jet samples. We find that most of the observables are highly correlated, and that their information content can be captured by a small set of observables. We also find that the correlations between observables are resilient to quenching effects and that specific pairs of observables exhaust the full sensitivity to quenching effects. The code, the datasets, and instructions on how to reproduce this work are also provided. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2304_07196 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Jet substructure observables for jet quenching in Quark Gluon Plasma: a Machine Learning driven analysis Romão, Miguel Crispim Milhano, José Guilherme van Leeuwen, Marco High Energy Physics - Phenomenology Nuclear Experiment Nuclear Theory We present a survey of a comprehensive set of jet substructure observables commonly used to study the modifications of jets resulting from interactions with the Quark Gluon Plasma in Heavy Ion Collisions. The \jewel{} event generator is used to produce simulated samples of quenched and unquenched jets. Three distinct analyses using Machine Learning techniques on the jet substructure observables have been performed to identify both linear and non-linear relations between the observables, and to distinguish the Quenched and Unquenched jet samples. We find that most of the observables are highly correlated, and that their information content can be captured by a small set of observables. We also find that the correlations between observables are resilient to quenching effects and that specific pairs of observables exhaust the full sensitivity to quenching effects. The code, the datasets, and instructions on how to reproduce this work are also provided. |
| title | Jet substructure observables for jet quenching in Quark Gluon Plasma: a Machine Learning driven analysis |
| topic | High Energy Physics - Phenomenology Nuclear Experiment Nuclear Theory |
| url | https://arxiv.org/abs/2304.07196 |