Harnessing data-driven methods for precise model independent event shape estimation in relativistic heavy-ion collisions
Year: 2025
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Basak, Dipankar, Hushnud, H., Dey, Kalyan
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7076954505
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