{
  "id": "2501.00374",
  "version": "v1",
  "published": "2024-12-31T09:55:21.000Z",
  "updated": "2024-12-31T09:55:21.000Z",
  "title": "Deep learning for exploring hadron-hadron interactions",
  "authors": [
    "Lingxiao Wang"
  ],
  "comment": "6 pages, 6 figures, contribution to the EMMI Workshop at the University of Wroclaw: Aspects of Criticality II. arXiv admin note: text overlap with arXiv:2410.03082",
  "categories": [
    "nucl-th",
    "hep-lat"
  ],
  "abstract": "In this proceeding, we introduce deep learning technologies for studying hadron-hadron interactions. To extract parameterized hadron interaction potentials from collision experiments, we employ a supervised learning approach using Femtoscopy data. The deep neural networks (DNNs) are trained to learn the inverse mapping from observations to potentials. To link between experiments and first-principles simulations, we further investigate hadronic interactions in Lattice QCD simulations from the HAL QCD method perspective. Using an unsupervised learning approach, we construct a model-free potential function with symmetric DNNs, aiming to learn hadron interactions directly from simulated correlation functions (equal-time Nambu-Bethe-Salpeter amplitudes). On both fronts, deep learning methods show great promise in advancing our understanding of hadron interactions.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-12-31T09:55:21.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "exploring hadron-hadron interactions",
      "deep learning",
      "extract parameterized hadron interaction potentials",
      "learning approach",
      "lattice qcd simulations"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 6,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}