Inferring Nuclear Radial Parameters from Initial-State Information in Heavy-Ion Collisions Using Deep Neural Networks
Abstract
The geometric structure of atomic nuclei plays an important role in shaping the initial conditions of high-energy nuclear collisions and the subsequent evolution of the quark--gluon plasma. In this work, we develop a multimodal deep-learning framework to infer the Woods--Saxon radius (R0) and diffuseness parameter (a) from initial-state information. Using the TRENTo model for relativistic 238U+238U collisions, we construct a dataset covering multiple combinations of R0 and a, and perform parameter regression by combining two-dimensional entropy-density distributions with event-level scalar observables, including multiplicity and eccentricities ε2--ε5. The results show that the proposed framework can effectively recover the Woods--Saxon parameters from initial-state information. Joint multi-event inputs suppress statistical fluctuations and improve regression performance by enhancing the extraction of collective information. The inversion accuracy exhibits a pronounced centrality dependence: R0 is best constrained in central collisions, whereas a shows the strongest sensitivity in the mid-peripheral region and becomes less well constrained in ultra-central and peripheral collisions. These findings indicate that the initial-state geometry retains useful information about nuclear radial structure and provide a baseline for future studies that incorporate transport evolution and experimental final-state observables.
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BibTeX
@misc{liu2026inferring,
title = {{Inferring Nuclear Radial Parameters from Initial-State Information in Heavy-Ion Collisions Using Deep Neural Networks}},
author = {Yang Liu and Yu Sha and Xiang Fan and Yan-Sheng Tu and Shi-Lei Tao and Qing Zhang and Jun-Qi Tao and Kai Zhou and Hua Zheng and Ben-Wei Zhang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.34072},
eprint = {2610.34072},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.34072},
}
RIS
TY - UNPB
TI - Inferring Nuclear Radial Parameters from Initial-State Information in Heavy-Ion Collisions Using Deep Neural Networks
AU - Yang Liu
AU - Yu Sha
AU - Xiang Fan
AU - Yan-Sheng Tu
AU - Shi-Lei Tao
AU - Qing Zhang
AU - Jun-Qi Tao
AU - Kai Zhou
AU - Hua Zheng
AU - Ben-Wei Zhang
PY - 2026
DA - 2026/10/25
PB - arXiv
DO - 10.48550/arxiv.2610.34072
UR - https://arxiv.org/abs/2610.34072
AB - The geometric structure of atomic nuclei plays an important role in shaping the initial conditions of high-energy nuclear collisions and the subsequent evolution of the quark--gluon plasma. In this work, we develop a multimodal deep-learning framework to infer the Woods--Saxon radius (R_0) and diffuseness parameter (a) from initial-state information. Using the TRENTo model for relativistic ^238U+^238U collisions, we construct a dataset covering multiple combinations of R_0 and a, and perform parameter regression by combining two-dimensional entropy-density distributions with event-level scalar observables, including multiplicity and eccentricities ε_2--ε_5. The results show that the proposed framework can effectively recover the Woods--Saxon parameters from initial-state information. Joint multi-event inputs suppress statistical fluctuations and improve regression performance by enhancing the extraction of collective information. The inversion accuracy exhibits a pronounced centrality dependence: R_0 is best constrained in central collisions, whereas a shows the strongest sensitivity in the mid-peripheral region and becomes less well constrained in ultra-central and peripheral collisions. These findings indicate that the initial-state geometry retains useful information about nuclear radial structure and provide a baseline for future studies that incorporate transport evolution and experimental final-state observables.
ER -