Classification of flavor dependence of Chiral Magnetic Effect with Deep Neural Network using multiple correlators

2025/07/25 by Dey, Somdeep, Saha, Abhisek, Sanyal, Soma
#FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #Nuclear Experiment (nucl-ex) #Nuclear Theory (nucl-th)

paper · doi:10.48550/arxiv.2507.19135

Abstract

We study the flavor dependence of the Chiral Magnetic Effect (CME) by analyzing two key charge-separation correlators used to characterize the charge separation effect: the conventional Δγ and the recently proposed R_ψ2. Using the AMPT (A Multiphase Transport) model with an initial-state centrality-dependent charge separation, we evaluate the sensitivity of these correlators to 2-flavor (u,d) and 3-flavor (u,d,s) quark scenarios. While both correlators exhibit modest flavor dependence in mid-central (30-50%) collisions, their discriminative power varies significantly with centrality and transverse momentum (p_T), limiting their utility disentangling the flavor dependent scenarios. To overcome these limitations, we develop a neural network classifier trained on final-state hadronic observables (e.g., dN_ch/dη, p_T spectra). The model achieves >90% accuracy in flavor classification by leveraging multi-observable correlations, with p_T-differential features proving particularly discriminative. Crucially, by incorporating background contributions directly into the training data, our approach provides more reliable flavor estimates than correlator-only methods.

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