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

TL;DR

This paper employs deep neural networks to classify flavor dependence in the Chiral Magnetic Effect, utilizing multiple correlators and hadronic observables to improve discrimination accuracy over traditional correlator-based methods.

Contribution

The study introduces a neural network approach that integrates multiple observables and background modeling to enhance flavor classification of the CME beyond existing correlator analyses.

Findings

Neural network achieves over 90% accuracy in flavor classification.

Multi-observable features, especially $p_T$-differential, are highly discriminative.

Incorporating background effects improves reliability of flavor estimates.

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 $\Delta\gamma$ and the recently proposed $R_{\psi_2}$. 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., $dN_{ch}/d\eta$, $p_T$ 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.