{
  "abstract": "Motivated by recent progress in data-driven approaches, we introduce a machine-learning (ML)-informed Debye mass, extracted from lattice-informed inputs, exclusively in the complex-valued heavy-quark Kent State University (KSU) potential. The resulting complex potential is used to solve the real-time Schrödinger equation within the quantum trajectories (QTraj) framework for the evolution of bottomonium in the quark-gluon plasma. We then compute the nuclear modification factors and double ratios for bottomonium $Υ(1S)$, $Υ(2S)$, and $Υ(3S)$ states in Pb-Pb collisions at $\\sqrt{s_{NN}} = 5.02$ TeV. We compare our ML-induced results with those from the original KSU model and with experimental measurements from ALICE, ATLAS, and CMS collaborations. We find that the machine-learned Debye mass leads to improved agreement with data, particularly for excited states, highlighting the utility of machine learning in modeling in-medium QCD effects.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.09047",
  "author_total": 5,
  "authors": [
    {
      "name": "Ajaharul Islam"
    },
    {
      "name": "Shibo Chen"
    },
    {
      "name": "Fu-Peng Li"
    },
    {
      "name": "Long-Gang Pang"
    },
    {
      "name": "Guang-You Qin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9cb3jc57byqak.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.09047",
  "fulltext_url": null,
  "id": "9cb3jc57byqak",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.09047",
  "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",
      "hep-ex"
    ],
    [
      "arxiv",
      "hep-ph"
    ],
    [
      "arxiv",
      "nucl-ex"
    ],
    [
      "arxiv",
      "nucl-th"
    ],
    [
      "openalex",
      "3106|High-Energy Particle Collisions Research"
    ]
  ],
  "openalex": "W7212074226",
  "pdf_url": "https://arxiv.org/pdf/2609.09047",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9cb3jc57byqak.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:37Z",
      "native_id": "2609.09047",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.09047"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    },
    {
      "field": "physics",
      "origin": "arxiv:nucl-ex",
      "subfield": "nuclear"
    }
  ],
  "title": "Bottomonium suppression with a machine-learning-informed Debye mass",
  "title_text": "Bottomonium suppression with a machine-learning-informed Debye mass",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:37Z",
  "url": "https://research.draconitic.com/paper/9cb3jc57byqak",
  "venue": "arXiv",
  "versions": []
}