A machine-learning method for the estimation of the background from jets misidentified as hadronic tau lepton decays
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 √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 pT. 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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BibTeX
@misc{gevorgyan2026machinelearning,
title = {{A machine-learning method for the estimation of the background from jets misidentified as hadronic tau lepton decays}},
author = {Arzunik Gevorgyan and Aram Hayrapetyan and Vladimir Makarenko and Armen Tumasyan and Wolfgang Adam and Lisa Benato and Thomas Bergauer and Marko Dragicevic and Stefan Gundacker and Ali Kaan Guven and Priya Sajid Hussain and Manfred Jeitler and Natascha Krammer and Ang Li and Dietrich Liko and Mark Nicholas Matthewman and Jochen Schieck and Robert Schoefbeck and Maryam Shooshtari and Mangesh Sonawane and Niels van den Bossche and Wolfgang Waltenberger and Claudia Wulz and Tahys Janssen and Daniel Ocampo Henao and Thomas van Laer and Pierre van Mechelen and Donya Ahmadi and Jas Bierkens and Nordin Breugelmans and Soumya Dansana and Alexandre de Moor and Martin Delcourt and Stef Duponcheel and Chirayu Gupta and Felix Johannes Heyen and Yanwen Hong and Kai Kang and Pavlo Kashko and Steven Lowette and Inna Makarenko and Saranya Nandakumar and Jeremi Niedziela and Stefaan Tavernier and Michael Tytgat and Gerrit van Onsem and Senne van Putte and Tiepolo Wybouw and Aya Beshr and Bugra Bilin and Franco Caviglia Roman and Barbara Clerbaux and Aloke Kumar Das and Isabelle de Bruyn and Gilles de Lentdecker and Eliott Ducarme and Hugues Evard and Laurent Favart and Ilia Kalaitzidou and Ali Khalilzadeh},
howpublished = {HAL},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.13634},
eprint = {2610.13634},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05829972v1},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A machine-learning method for the estimation of the background from jets misidentified as hadronic tau lepton decays
AU - Arzunik Gevorgyan
AU - Aram Hayrapetyan
AU - Vladimir Makarenko
AU - Armen Tumasyan
AU - Wolfgang Adam
AU - Lisa Benato
AU - Thomas Bergauer
AU - Marko Dragicevic
AU - Stefan Gundacker
AU - Ali Kaan Guven
AU - Priya Sajid Hussain
AU - Manfred Jeitler
AU - Natascha Krammer
AU - Ang Li
AU - Dietrich Liko
AU - Mark Nicholas Matthewman
AU - Jochen Schieck
AU - Robert Schoefbeck
AU - Maryam Shooshtari
AU - Mangesh Sonawane
AU - Niels van den Bossche
AU - Wolfgang Waltenberger
AU - Claudia Wulz
AU - Tahys Janssen
AU - Daniel Ocampo Henao
AU - Thomas van Laer
AU - Pierre van Mechelen
AU - Donya Ahmadi
AU - Jas Bierkens
AU - Nordin Breugelmans
AU - Soumya Dansana
AU - Alexandre de Moor
AU - Martin Delcourt
AU - Stef Duponcheel
AU - Chirayu Gupta
AU - Felix Johannes Heyen
AU - Yanwen Hong
AU - Kai Kang
AU - Pavlo Kashko
AU - Steven Lowette
AU - Inna Makarenko
AU - Saranya Nandakumar
AU - Jeremi Niedziela
AU - Stefaan Tavernier
AU - Michael Tytgat
AU - Gerrit van Onsem
AU - Senne van Putte
AU - Tiepolo Wybouw
AU - Aya Beshr
AU - Bugra Bilin
AU - Franco Caviglia Roman
AU - Barbara Clerbaux
AU - Aloke Kumar Das
AU - Isabelle de Bruyn
AU - Gilles de Lentdecker
AU - Eliott Ducarme
AU - Hugues Evard
AU - Laurent Favart
AU - Ilia Kalaitzidou
AU - Ali Khalilzadeh
PY - 2026
DA - 2026/10/09
PB - HAL
DO - 10.48550/arxiv.2610.13634
UR - https://hal.science/hal-05829972v1
AB - 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 √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_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.
ER -