{
  "abstract": "Monte Carlo simulations are used extensively in high-energy particle physics analyses. However, an incomplete description of the underlying physics can lead to significant discrepancies between observables measured in simulated and collision data. To mitigate potential biases arising from such mismodelling, it is essential to calibrate simulations to data. This paper presents two novel calibration methods based on machine-learning techniques: a reweighting approach, which employs a classifier to learn the ratio of probability density functions between simulation and data; and a normalising-flow approach, which learns a high-dimensional transformation to map simulation to data. Compared to traditional calibration methods, both approaches offer continuous, unbinned corrections across high-dimensional feature spaces, enabling improved global agreement with data. The techniques are demonstrated in the context of correcting the features of simulated electromagnetic showers in the CMS calorimeters, using proton-proton collision data collected during 2022 at $\\sqrt{s}$ = 13.6 TeV, corresponding to an integrated luminosity of 26.7 fb$^{-1}$. The strengths and limitations of the two methods are compared.",
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  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.15377",
  "author_total": 2538,
  "authors": [
    {
      "name": "Arzunik Gevorgyan"
    },
    {
      "name": "Aram Hayrapetyan"
    },
    {
      "name": "Vladimir Makarenko"
    },
    {
      "name": "Armen Tumasyan"
    },
    {
      "name": "Priya Sajid Hussain"
    },
    {
      "name": "Mangesh Sonawane"
    },
    {
      "name": "Wolfgang Adam"
    },
    {
      "name": "Lisa Benato"
    },
    {
      "name": "Thomas Bergauer"
    },
    {
      "name": "Marko Dragicevic"
    },
    {
      "name": "Stefan Gundacker"
    },
    {
      "name": "Ali Kaan Guven"
    },
    {
      "name": "Manfred Jeitler"
    },
    {
      "name": "Natascha Krammer"
    },
    {
      "name": "Ang Li"
    },
    {
      "name": "Dietrich Liko"
    },
    {
      "name": "Mark Nicholas Matthewman"
    },
    {
      "name": "Andreas Pfeiffer"
    },
    {
      "name": "Jochen Schieck"
    },
    {
      "name": "Robert Schoefbeck"
    },
    {
      "name": "Maryam Shooshtari"
    },
    {
      "name": "Niels van den Bossche"
    },
    {
      "name": "Wolfgang Waltenberger"
    },
    {
      "name": "Claudia Wulz"
    },
    {
      "name": "Tahys Janssen"
    },
    {
      "name": "Daniel Ocampo Henao"
    },
    {
      "name": "Thomas van Laer"
    },
    {
      "name": "Pierre van Mechelen"
    },
    {
      "name": "Donya Ahmadi"
    },
    {
      "name": "Jas Bierkens"
    },
    {
      "name": "Nordin Breugelmans"
    },
    {
      "name": "Soumya Dansana"
    },
    {
      "name": "Alexandre de Moor"
    },
    {
      "name": "Martin Delcourt"
    },
    {
      "name": "Stef Duponcheel"
    },
    {
      "name": "Chirayu Gupta"
    },
    {
      "name": "Felix Johannes Heyen"
    },
    {
      "name": "Yanwen Hong"
    },
    {
      "name": "Kai Kang"
    },
    {
      "name": "Pavlo Kashko"
    },
    {
      "name": "Steven Lowette"
    },
    {
      "name": "Inna Makarenko"
    },
    {
      "name": "Saranya Nandakumar"
    },
    {
      "name": "Jeremi Niedziela"
    },
    {
      "name": "Stefaan Tavernier"
    },
    {
      "name": "Michael Tytgat"
    },
    {
      "name": "Gerrit van Onsem"
    },
    {
      "name": "Senne van Putte"
    },
    {
      "name": "Tiepolo Wybouw"
    },
    {
      "name": "Aya Beshr"
    },
    {
      "name": "Bugra Bilin"
    },
    {
      "name": "Franco Caviglia Roman"
    },
    {
      "name": "Barbara Clerbaux"
    },
    {
      "name": "Aloke Kumar Das"
    },
    {
      "name": "Isabelle de Bruyn"
    },
    {
      "name": "Gilles de Lentdecker"
    },
    {
      "name": "Eliott Ducarme"
    },
    {
      "name": "Hugues Evard"
    },
    {
      "name": "Laurent Favart"
    },
    {
      "name": "Ilia Kalaitzidou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6r91b7y5bz5ea.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.15377",
  "fulltext_url": null,
  "id": "6r91b7y5bz5ea",
  "issns": [],
  "landing_url": "https://hal.science/hal-05763105v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
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      "hep-ex"
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      "openalex",
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  "openalex": "W7213322660",
  "pdf_url": "https://arxiv.org/pdf/2609.15377",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6r91b7y5bz5ea.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:40Z",
      "native_id": "hal-05763105",
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      "url": "https://hal.science/hal-05763105v1"
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    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "2609.15377",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.15377"
    }
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  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Calibration of electromagnetic shower features in the CMS calorimeter with machine-learning techniques",
  "title_text": "Calibration of electromagnetic shower features in the CMS calorimeter with machine-learning techniques",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:25Z",
  "url": "https://research.draconitic.com/paper/6r91b7y5bz5ea",
  "venue": "HAL",
  "versions": []
}