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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2402.10945 |
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| _version_ | 1866929249049378816 |
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| author | Mengel, Tanner Steffanic, Patrick Hughes, Charles Da Silva, Antonio Carlos Oliveira Nattrass, Christine |
| author_facet | Mengel, Tanner Steffanic, Patrick Hughes, Charles Da Silva, Antonio Carlos Oliveira Nattrass, Christine |
| contents | Jet measurements in heavy ion collisions at low jet momentum can provide constraints on the properties of the quark gluon plasma but are overwhelmed by a significant, fluctuating background. We build upon our previous work which demonstrated the ability of the jet multiplicity method to extend jet measurements into the domain of low jet momentum [1, Mengel:2023]. We extend this method to a wide range of jet resolution parameters. We investigate the over-complexity of non-interpretable machine learning used to tackle the problem of jet background subtraction through network optimization. Finally, we show that the resulting shallow neural network is able to learn the underlying relationship between jet multiplicity and background fluctuations, with a lesser complexity, reinforcing the utility of interpretable methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10945 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multiplicity Based Background Subtraction for Jets in Heavy Ion Collisions Mengel, Tanner Steffanic, Patrick Hughes, Charles Da Silva, Antonio Carlos Oliveira Nattrass, Christine High Energy Physics - Experiment Nuclear Experiment Jet measurements in heavy ion collisions at low jet momentum can provide constraints on the properties of the quark gluon plasma but are overwhelmed by a significant, fluctuating background. We build upon our previous work which demonstrated the ability of the jet multiplicity method to extend jet measurements into the domain of low jet momentum [1, Mengel:2023]. We extend this method to a wide range of jet resolution parameters. We investigate the over-complexity of non-interpretable machine learning used to tackle the problem of jet background subtraction through network optimization. Finally, we show that the resulting shallow neural network is able to learn the underlying relationship between jet multiplicity and background fluctuations, with a lesser complexity, reinforcing the utility of interpretable methods. |
| title | Multiplicity Based Background Subtraction for Jets in Heavy Ion Collisions |
| topic | High Energy Physics - Experiment Nuclear Experiment |
| url | https://arxiv.org/abs/2402.10945 |