We study the flavor dependence of the chiral magnetic effect by analyzing two key charge-separation correlators used to characterize the charge separation effect: the conventional $\gamma$ correlator and the recently proposed $R_{{\psi }_2}$. Using A Multi-Phase Transport model with an initial-state centrality-dependent charge separation, we evaluate the sensitivity of these correlators to two-flavor ($u,d$) and three-flavor ($u,d,s$) quark scenarios. While both correlators exhibit modest flavor dependence in midcentral (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., $d{N}_{\mathrm{ch}}/d\eta , p_T$ spectra). The model achieves $>90\%$ accuracy in flavor classification by leveraging multiobservable 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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• Particle correlations & fluctuations
• Quark-gluon plasma
• Relativistic heavy-ion collisions
• 190 ≤ A ≤ 219
• 90 ≤ A ≤ 149
• Convolutional neural networks