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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2507.19135 |
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| _version_ | 1866909705200205824 |
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| author | Dey, Somdeep Saha, Abhisek Sanyal, Soma |
| author_facet | Dey, Somdeep Saha, Abhisek Sanyal, Soma |
| contents | 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 $R_{ψ_2}$. 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., $dN_{ch}/dη$, $p_T$ 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19135 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Classification of flavor dependence of Chiral Magnetic Effect with Deep Neural Network using multiple correlators Dey, Somdeep Saha, Abhisek Sanyal, Soma Nuclear Theory High Energy Physics - Phenomenology Nuclear Experiment 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 $R_{ψ_2}$. 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., $dN_{ch}/dη$, $p_T$ 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. |
| title | Classification of flavor dependence of Chiral Magnetic Effect with Deep Neural Network using multiple correlators |
| topic | Nuclear Theory High Energy Physics - Phenomenology Nuclear Experiment |
| url | https://arxiv.org/abs/2507.19135 |