{
  "annotation_id": "7aa45204-7e5b-4324-aef1-ff3e8735be53",
  "date_created": "2026-03-02T18:00:07.759000Z",
  "date_modified": "2026-03-02T18:00:07.759000Z",
  "file_hash": "33df0d1598e1dc82675fb4236cdd35d284b154d917f29623b29c6c0ec657c8ae",
  "private": false,
  "record": {
    "abstract": "A neural-network model is developed to reproduce the differences between\nexperimental nuclear mass-excess values and the theoretical values given by the\nFinite Range Droplet Model. The results point to the existence of subtle\nregularities of nuclear structure not yet contained in the best\nmicroscopic/phenomenological models of atomic masses. Combining the FRDM and\nthe neural-network model, we create a hybrid model with improved predictive\nperformance on nuclear-mass systematics and related quantities.",
    "arxiv_id": "nucl-th/0511088",
    "authors": [
      "S. Athanassopoulos",
      "E. Mavrommatis",
      "K. A. Gernoth",
      "J. W. Clark"
    ],
    "categories": [
      "nucl-th"
    ],
    "title": "Nuclear mass systematics by complementing the Finite Range Droplet Model   with neural networks",
    "url": "https://arxiv.org/abs/nucl-th/0511088"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "4336e555-ed54-4615-a499-f1746971811b",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}