{
  "abstract": "Electromagnetic calorimeters provide essential information for reconstructing and selecting both Standard Model (SM) and potential beyond the SM physics events at high-energy particle colliders. The fine-grained segmentation of modern calorimeters captures rich information about the internal structure of particle showers, much of which is discarded by conventional high-level reconstruction methods. In this work, we leverage calorimeter cell-level information to classify highly collimated diphoton signatures, arising from the decay of light axion-like particles, from isolated single-photon showers. We systematically compare a range of machine learning architectures, spanning high-level, shower shape variable-based approaches and direct cell-level methods. Cell-level machine learning shows significantly superior classification ability, with a Transformer in particular representing the best performance among six different architectures studied, and an MLP Mixer representing a resource-constrained alternative for potential real-time, trigger-level applications. Beyond classification, the Transformer model developed enables direct invariant mass regression from calorimeter cells, improving the characterization of light resonances and providing an additional handle in reducing the $π^0$ and $η$ fake photon backgrounds. These results demonstrate that cell-level machine learning methods can extend calorimeter-based particle identification and performance well beyond the capabilities of current conventional techniques.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.08175",
  "author_total": 11,
  "authors": [
    {
      "name": "Gabriel Matos"
    },
    {
      "name": "Lauren Larson"
    },
    {
      "name": "Abhilasha Dave"
    },
    {
      "name": "Maria Bressan"
    },
    {
      "name": "Azal Amer"
    },
    {
      "name": "Cindy Liu"
    },
    {
      "name": "Nikiforos Nikiforou"
    },
    {
      "name": "Jonathan Long"
    },
    {
      "name": "Timothy Andeen"
    },
    {
      "name": "John Parsons"
    },
    {
      "name": "Julia Gonski"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aasawy650nzbw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.08175",
  "fulltext_url": null,
  "id": "aasawy650nzbw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.08175",
  "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"
    ],
    [
      "openalex",
      "3106|Particle Detector Development and Performance"
    ]
  ],
  "openalex": "W7167922580",
  "pdf_url": "https://arxiv.org/pdf/2607.08175",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/aasawy650nzbw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:35Z",
      "native_id": "2607.08175",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.08175"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Transformer-based machine learning using low-level calorimeter signals for collimated photon identification at collider experiments",
  "title_text": "Transformer-based machine learning using low-level calorimeter signals for collimated photon identification at collider experiments",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:35Z",
  "url": "https://research.draconitic.com/paper/aasawy650nzbw",
  "venue": "arXiv",
  "versions": []
}