{
  "abstract": "Potential energy surfaces and collective inertial functions are essential microscopic inputs for describing nuclear large-amplitude collective motions such as rotation, vibration, and fission. We develop the Nuclear Collective Generator (NCG), a machine-learning framework that predicts the collective potential and six collective inertial functions on the quadrupole deformation $(β,γ)$ plane from proton and neutron numbers and shell-effect descriptors, providing the microscopic inputs for the five-dimensional collective Hamiltonian (5DCH) used to describe low-lying spectra in even-even nuclei. The NCG combines weighted supervised learning, adversarial refinement, and ensemble averaging to improve reconstruction fidelity and prediction stability. Across 568 even-even nuclei, the reconstructed collective potentials reproduce the covariant density functional theory (CDFT) results with a mean root-mean-square deviation of 0.58~MeV. When propagated through the 5DCH solver, the NCG inputs reproduce the global systematics of equilibrium deformations, low-lying excitation spectra, and electric-quadrupole transition strengths. Near the $Z=82$ shell closure, the NCG softens the collective potential along the $γ$ direction, reducing the overestimated collectivity of the original CDFT+5DCH calculations and bringing the $B(E2)$ values closer to experimental data. These results demonstrate that the NCG provides an accurate surrogate for global microscopic collective calculations while retaining their essential physical content.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.25888",
  "author_total": 4,
  "authors": [
    {
      "name": "Dan Shi"
    },
    {
      "name": "Zu-Xing Yang"
    },
    {
      "name": "Xiao-Hua Fan"
    },
    {
      "name": "Zhi-Pan Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0x32j970pytzt.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.25888",
  "fulltext_url": null,
  "id": "0x32j970pytzt",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.25888",
  "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|Nuclear physics research studies"
    ]
  ],
  "openalex": "W7214099654",
  "pdf_url": "https://arxiv.org/pdf/2609.25888",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0x32j970pytzt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:39Z",
      "native_id": "2609.25888",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.25888"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:nucl-th",
      "subfield": "nuclear"
    }
  ],
  "title": "Machine-learning modeling of nuclear collective observables and low-lying spectra",
  "title_text": "Machine-learning modeling of nuclear collective observables and low-lying spectra",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:39Z",
  "url": "https://research.draconitic.com/paper/0x32j970pytzt",
  "venue": "arXiv",
  "versions": []
}