Somdeep Dey | Searcharxiv

arXiv2026

Probing the chiral magnetic effect via transverse spherocity event classification in relativistic heavy-ion collisions

We present the first study of the Chiral Magnetic Effect (CME) using transverse spherocity as an event-shape classifier in Pb+Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV, simulated with the A Multi-Phase Transport (AMPT) model with a realistic CME implementation. Transverse spherocity separates events into jetty and isotropic topologies based on the geometric distribution of transverse momentum. Unlike traditional event shape engineering methods, which use the flow vector as an event classifier that is itself contaminated by the very backgrounds it is intended to suppress, spherocity provides a cleaner, geometry-driven classification that avoids this circular limitation. CME inclusion shifts the spherocity distribution toward more isotropic events, confirming its sensitivity to CME-induced charge separation. The charge-dependent azimuthal correlator $\Delta\gamma$ and correlated background coupled with elliptic flow are consistently higher in jetty events. The scaled ratio $\Delta\gamma/v_2$ shows enhanced values for isotropic events, confirming effective background suppression after elliptic flow scaling. Our results demonstrate that isotropic event selection via transverse spherocity provides a cleaner and more reliable environment for CME searches by simultaneously suppressing flow-driven and resonance-decay backgrounds, making it a powerful complementary method to existing flow-vector-based methods.

nucl-ex↗

arXiv2025

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

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.

nucl-th↗