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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.19389 |
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| _version_ | 1866910719914541056 |
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| author | Qureshi, Umar Sohail Elayavalli, Raghav Kunnawalkam |
| author_facet | Qureshi, Umar Sohail Elayavalli, Raghav Kunnawalkam |
| contents | Measurements of jet substructure in ultra-relativistic heavy-ion collisions indicate that interactions with the quark-gluon plasma quench the jet showering process. Modern data-driven methods have shown promise in probing these modifications in the jet's hard substructure. In this Letter, we present a machine learning framework to identify quenched jets while accounting for pileup, uncorrelated soft particle background, and detector effects; a more experimentally realistic and challenging scenario than previously addressed. Our approach leverages an interpretable sequential attention-based mechanism that integrates representations of individual jet constituents alongside global jet observables as features. The framework sets a new benchmark for tagging quenched jets with reduced model dependence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_19389 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model-Agnostic Tagging of Quenched Jets in Heavy-Ion Collisions Qureshi, Umar Sohail Elayavalli, Raghav Kunnawalkam High Energy Physics - Phenomenology Measurements of jet substructure in ultra-relativistic heavy-ion collisions indicate that interactions with the quark-gluon plasma quench the jet showering process. Modern data-driven methods have shown promise in probing these modifications in the jet's hard substructure. In this Letter, we present a machine learning framework to identify quenched jets while accounting for pileup, uncorrelated soft particle background, and detector effects; a more experimentally realistic and challenging scenario than previously addressed. Our approach leverages an interpretable sequential attention-based mechanism that integrates representations of individual jet constituents alongside global jet observables as features. The framework sets a new benchmark for tagging quenched jets with reduced model dependence. |
| title | Model-Agnostic Tagging of Quenched Jets in Heavy-Ion Collisions |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2411.19389 |