{
  "abstract": "Backgrounds arising from quark- and gluon-initiated jets misidentified as hadronically decaying tau leptons constitute a major experimental challenge for analyses involving tau leptons. The MUltivariate Fake-Factor INference (MUFFIN) approach, a machine-learning-based method for estimating this background directly from observed events, is presented. Boosted decision tree reweighting is used to learn multidimensional, per-object correction factors directly from observed data, thereby modelling the jet to hadronic tau lepton misidentification probability across the kinematic and topological phase space without relying on low-dimensional parametrisations. Performance studies are presented using proton-proton collision data collected by the CMS experiment at the CERN LHC at $\\sqrt{s}$ = 13.6 TeV, corresponding to an integrated luminosity of 62.4 fb$^{-1}$. Compared to low-dimensional parametrisation methods, improved modelling of key kinematic observables is achieved, particularly in regions of high $p_\\mathrm{T}$. The improved modelling leads to a reduction of up to 70% in the systematic uncertainty associated with jets being misidentified as hadronically decaying tau leptons. The MUFFIN method provides a robust and scalable method for background estimation in precision measurements and searches for beyond-the-standard-model physics involving tau lepton final states.",
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  "arxiv": "2610.13634",
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  "authors": [
    {
      "name": "Arzunik Gevorgyan"
    },
    {
      "name": "Aram Hayrapetyan"
    },
    {
      "name": "Vladimir Makarenko"
    },
    {
      "name": "Armen Tumasyan"
    },
    {
      "name": "Wolfgang Adam"
    },
    {
      "name": "Lisa Benato"
    },
    {
      "name": "Thomas Bergauer"
    },
    {
      "name": "Marko Dragicevic"
    },
    {
      "name": "Stefan Gundacker"
    },
    {
      "name": "Ali Kaan Guven"
    },
    {
      "name": "Priya Sajid Hussain"
    },
    {
      "name": "Manfred Jeitler"
    },
    {
      "name": "Natascha Krammer"
    },
    {
      "name": "Ang Li"
    },
    {
      "name": "Dietrich Liko"
    },
    {
      "name": "Mark Nicholas Matthewman"
    },
    {
      "name": "Jochen Schieck"
    },
    {
      "name": "Robert Schoefbeck"
    },
    {
      "name": "Maryam Shooshtari"
    },
    {
      "name": "Mangesh Sonawane"
    },
    {
      "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"
    },
    {
      "name": "Ali Khalilzadeh"
    }
  ],
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  "doi": "10.48550/arxiv.2610.13634",
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  "pdf_url": "https://arxiv.org/pdf/2610.13634",
  "pmcid": null,
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  "published": "2026-10-09",
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  "ris": "https://research.draconitic.com/paper/amjxx5fvzp8k6.ris",
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    {
      "field": "physics",
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  "title": "A machine-learning method for the estimation of the background from jets misidentified as hadronic tau lepton decays",
  "title_text": "A machine-learning method for the estimation of the background from jets misidentified as hadronic tau lepton decays",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:36Z",
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