{
  "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 ($R_0$) and diffuseness parameter ($a$) from initial-state information. Using the TRENTo model for relativistic $^{238}$U+$^{238}$U 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 $\\varepsilon_2$--$\\varepsilon_5$. 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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.34072",
  "author_total": 10,
  "authors": [
    {
      "name": "Yang Liu"
    },
    {
      "name": "Yu Sha"
    },
    {
      "name": "Xiang Fan"
    },
    {
      "name": "Yan-Sheng Tu"
    },
    {
      "name": "Shi-Lei Tao"
    },
    {
      "name": "Qing Zhang"
    },
    {
      "name": "Jun-Qi Tao"
    },
    {
      "name": "Kai Zhou"
    },
    {
      "name": "Hua Zheng"
    },
    {
      "name": "Ben-Wei Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6njcc56vaps5y.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.34072",
  "fulltext_url": null,
  "id": "6njcc56vaps5y",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.34072",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "nucl-th"
    ],
    [
      "openalex",
      "3106|High-Energy Particle Collisions Research"
    ]
  ],
  "openalex": "W7223798557",
  "pdf_url": "https://arxiv.org/pdf/2610.34072",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6njcc56vaps5y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:35Z",
      "native_id": "2610.34072",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.34072"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:nucl-th",
      "subfield": "nuclear"
    }
  ],
  "title": "Inferring Nuclear Radial Parameters from Initial-State Information in Heavy-Ion Collisions Using Deep Neural Networks",
  "title_text": "Inferring Nuclear Radial Parameters from Initial-State Information in Heavy-Ion Collisions Using Deep Neural Networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:35Z",
  "url": "https://research.draconitic.com/paper/6njcc56vaps5y",
  "venue": "arXiv",
  "versions": []
}