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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.05808 |
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| _version_ | 1866912667387559936 |
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| author | Guo, Shuang Wang, Lingxiao Zhou, Kai Ma, Guo-Liang |
| author_facet | Guo, Shuang Wang, Lingxiao Zhou, Kai Ma, Guo-Liang |
| contents | The search for the chiral magnetic effect (CME) in relativistic heavy-ion collisions (HICs) is challenged by significant background contamination. We present a novel deep learning approach based on a U-Net architecture to time-reversely unfold the dynamics of CME-related charge separation, enabling the reconstruction of the physics signal across the entire evolution of HICs. Trained on the events simulated by a multi-phase transport model with different cases of CME settings, our model learns to recover the charge separation based on final-state transverse momentum distributions at either the quark-gloun plasma freeze-out or hadronic freeze-out. This devises a methodological tool for the study of CME and underscores the promise of deep learning approaches in retrieving physics signals in HICs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05808 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions Guo, Shuang Wang, Lingxiao Zhou, Kai Ma, Guo-Liang Nuclear Theory High Energy Physics - Phenomenology Nuclear Experiment The search for the chiral magnetic effect (CME) in relativistic heavy-ion collisions (HICs) is challenged by significant background contamination. We present a novel deep learning approach based on a U-Net architecture to time-reversely unfold the dynamics of CME-related charge separation, enabling the reconstruction of the physics signal across the entire evolution of HICs. Trained on the events simulated by a multi-phase transport model with different cases of CME settings, our model learns to recover the charge separation based on final-state transverse momentum distributions at either the quark-gloun plasma freeze-out or hadronic freeze-out. This devises a methodological tool for the study of CME and underscores the promise of deep learning approaches in retrieving physics signals in HICs. |
| title | Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions |
| topic | Nuclear Theory High Energy Physics - Phenomenology Nuclear Experiment |
| url | https://arxiv.org/abs/2507.05808 |