Harnessing data-driven methods for precise model independent event shape estimation in relativistic heavy-ion collisions
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917099369136128 |
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| author | Basak, Dipankar Hushnud, H. Dey, Kalyan |
| author_facet | Basak, Dipankar Hushnud, H. Dey, Kalyan |
| contents | This study demonstrates the application of supervised machine learning (ML) techniques to distinguish between isotropic and jet-like event topologies in heavy-ion collisions via the spherocity observable. State-of-the-art ML algorithms, optimized through systematic hyperparameter tuning, are employed to predict both traditional transverse spherocity $S_{0}$ and unweighted transverse spherocity $S_{0}^{p_{\rm T}=1}$ directly from raw event data. Moreover, the results from this study demonstrated that our approach remains largely model-independent, underscoring its potential applicability in future experimental heavy-ion physics analyses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13349 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Harnessing data-driven methods for precise model independent event shape estimation in relativistic heavy-ion collisions Basak, Dipankar Hushnud, H. Dey, Kalyan High Energy Physics - Phenomenology This study demonstrates the application of supervised machine learning (ML) techniques to distinguish between isotropic and jet-like event topologies in heavy-ion collisions via the spherocity observable. State-of-the-art ML algorithms, optimized through systematic hyperparameter tuning, are employed to predict both traditional transverse spherocity $S_{0}$ and unweighted transverse spherocity $S_{0}^{p_{\rm T}=1}$ directly from raw event data. Moreover, the results from this study demonstrated that our approach remains largely model-independent, underscoring its potential applicability in future experimental heavy-ion physics analyses. |
| title | Harnessing data-driven methods for precise model independent event shape estimation in relativistic heavy-ion collisions |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2508.13349 |