{
  "abstract": "The increasing event complexity and data rates foreseen for future LHC coinditions motivate a transition from selecting proton-proton ($pp$) collision events by reconstructing predefined signal candidates to constructing a reduced data stream by interpreting the relevant event features in a signal-agnostic way. We explore the capabilities of deep-learning-based architectures on heavy-flavour physics using LHCb as a case study. Graph-based full-event interpretation techniques address the problem by representing visible products of $pp$ collisions as nodes of a graph, and reconstructing heavy-hadron decay chains hierarchically. A new neutral-particle-aware architecture extends the input data from charged tracks to calorimeter clusters, and uses metric learning to clusterize particles from the same decay chain in a common latent space. Tests on inclusive simulated $pp\\to b\\bar b$ events show that this approach retains its ability to reconstruct decay chains across a broad range of topologies and multiplicities. We discuss the resulting physics opportunities, with a focus on inclusive $b\\to s\\ell\\ell$ measurements and on searches for exotic hadrons.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.16021",
  "author_total": 5,
  "authors": [
    {
      "name": "Matteo Barbieri"
    },
    {
      "name": "Marta Calvi"
    },
    {
      "name": "Elena Graverini"
    },
    {
      "name": "Rafael Silva Coutinho"
    },
    {
      "name": "Felipe Luan Souza de Almeida"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/65np2by0wmp97.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.16021",
  "fulltext_url": null,
  "id": "65np2by0wmp97",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.16021",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "hep-ex"
    ],
    [
      "openalex",
      "3106|Particle physics theoretical and experimental studies"
    ]
  ],
  "openalex": "W7221801154",
  "pdf_url": "https://arxiv.org/pdf/2610.16021",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/65np2by0wmp97.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:06Z",
      "native_id": "2610.16021",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.16021"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "A new AI-based Full Event Interpretation model for flavour physics",
  "title_text": "A new AI-based Full Event Interpretation model for flavour physics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:06Z",
  "url": "https://research.draconitic.com/paper/65np2by0wmp97",
  "venue": "arXiv",
  "versions": []
}