Quantifying vacuum-like jets in heavy-ion collisions: a Machine Learning study
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arXiv
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| Format: | Preprint |
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2025
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| author | Romão, Miguel Crispim Gonçalves, João Arruda Milhano, José Guilherme |
| author_facet | Romão, Miguel Crispim Gonçalves, João Arruda Milhano, José Guilherme |
| contents | The modification of jets by interaction with the Quark Gluon Plasma has been extensively established through the comparison of observables computed for samples of jets produced in nucleus-nucleus collisions and proton-proton collisions. The presence of vacuum-like jets, jets that experienced little interaction with the Quark Gluon Plasma, in the nucleus-nucleus samples dilutes the overall observed modification hindering the detailed study of the underlying physical mechanisms. The ability to ascertain on a jet-by-jet basis the degree of modification of a jet would be an invaluable step in overcoming this limitation. We consider a Transformer classifier, trained on a low-level representation of jets given by the 4-momenta of all its constituents. We show that the Transformer is able to capture discriminating information not accessible to other architectures which use high-level physical observables as input. The Transformer allows us to identify, in the experimentally relevant case where both medium response and underlying event contamination are accounted for, a class of jets that have been unequivocally modified. Further, we perform a robust estimate of the upper bound for the fraction of jets in nucleus-nucleus collisions that are, for all purposes, indistinguishable from those produced in proton-proton collisions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10724 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantifying vacuum-like jets in heavy-ion collisions: a Machine Learning study Romão, Miguel Crispim Gonçalves, João Arruda Milhano, José Guilherme High Energy Physics - Phenomenology The modification of jets by interaction with the Quark Gluon Plasma has been extensively established through the comparison of observables computed for samples of jets produced in nucleus-nucleus collisions and proton-proton collisions. The presence of vacuum-like jets, jets that experienced little interaction with the Quark Gluon Plasma, in the nucleus-nucleus samples dilutes the overall observed modification hindering the detailed study of the underlying physical mechanisms. The ability to ascertain on a jet-by-jet basis the degree of modification of a jet would be an invaluable step in overcoming this limitation. We consider a Transformer classifier, trained on a low-level representation of jets given by the 4-momenta of all its constituents. We show that the Transformer is able to capture discriminating information not accessible to other architectures which use high-level physical observables as input. The Transformer allows us to identify, in the experimentally relevant case where both medium response and underlying event contamination are accounted for, a class of jets that have been unequivocally modified. Further, we perform a robust estimate of the upper bound for the fraction of jets in nucleus-nucleus collisions that are, for all purposes, indistinguishable from those produced in proton-proton collisions. |
| title | Quantifying vacuum-like jets in heavy-ion collisions: a Machine Learning study |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2511.10724 |