A machine-learning method for the estimation of the background from jets misidentified as hadronic tau lepton decays
Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Author: CMS Collaboration
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: Ministerio de Ciencia, Tecnología e Innovación, National Science Foundation, U.S. Department of Energy, CERN, Türkiye Enerji, Nükleer ve Maden Araştırma Kurumu, European Commission, Department of Science and Technology, Ministry of Science and Technology, India, Science Foundation Ireland, Deutsche Forschungsgemeinschaft, Fundação de Amparo à Pesquisa do Estado de São Paulo, National Natural Science Foundation of China, National Science Council, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Austrian Science Fund, Fonds De La Recherche Scientifique - FNRS, Fonds Wetenschappelijk Onderzoek, Ministerio de Economía y Competitividad, Bulgarian National Science Fund, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, Secretaría de Educación Superior, Ciencia, Tecnología e Innovación, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, Ministry of Education, India, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, National Academy of Sciences of Ukraine, Centre National de la Recherche Scientifique, Shota Rustaveli National Science Foundation, Universidad Autónoma de San Luis Potosí, Benemérita Universidad Autónoma de Puebla, Pakistan Atomic Energy Commission, Ministry of Science,Technology and Research, Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, Ministry of Science and ICT, South Korea, Narodowa Agencja Wymiany Akademickiej, Ministry of Higher Education, Science, Research and Innovation, Thailand, National Science and Technology Council, Science and Technology Facilities Council, Fundação para a Ciência e a Tecnologia, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Agencia Estatal de Investigación, Institut National de Physique Nucléaire et de Physique des Particules
Awards: G196419191, The strong interaction at the frontier of knowledge: fundamental research and applications, Direct and indirect searches for new physics in events with top quarks using LHC proton-proton collisions at the CMS detector, Advanced Multi-Variate Analysis for New Physics Searches at the LHC, G4294835076, International Training Network for Statistics in High Energy Physics and Society, G6649312515, G6837507712, G7651396250
OpenAlex ID: W7161472063