Harnessing data-driven methods for precise model independent event shape estimation in relativistic heavy-ion collisions

TL;DR

This paper applies supervised machine learning to classify event topologies in heavy-ion collisions using spherocity, achieving a model-independent approach that enhances event shape analysis accuracy.

Contribution

The study introduces a machine learning framework that predicts spherocity observables directly from raw data, demonstrating a largely model-independent method for event shape estimation.

Findings

ML algorithms accurately predict spherocity observables

Approach remains largely model-independent

Potential for improved experimental analysis

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.