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Model-agnostic search for dijet resonances with anomalous jet substructure in proton–proton collisions at s = 13 TeV

Title: Model-agnostic search for dijet resonances with anomalous jet substructure in proton–proton collisions at s = 13 TeV
Contributors: SC; Austrian Federal Ministry of Education, Science and Research; Austrian Science Fund; Belgian Fonds de la Recherche Scientifique; Belgian Fonds voor Wetenschappelijk Onderzoek; CNPq; CAPES; FAPERJ; FAPERGS; FAPESP; Bulgarian Ministry of Education and Science; Bulgarian National Science Fund; CERN; Chinese Academy of Sciences; Ministry of Science and Technology; Chinese National Natural Science Foundation of China; Colombian Funding Agency; Croatian Ministry of Science, Education and Sport; Croatian Science Foundation; Research and Innovation Foundation; SENESCYT; Estonian Research Council; Ministry of Education and Research; Academy of Finland; Finnish Ministry of Education and Culture; Helsinki Institute of Physics; Institut National de Physique Nucléaire et de Physique des Particules; Centre National de la Recherche Scientifique; Commissariat à l’Énergie Atomique et aux Énergies Alternatives; Shota Rustaveli National Science Foundation; Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; Helmholtz-Gemeinschaft Deutscher Forschungszentren; General Secretariat for Research and Innovation; National Research, Development and Innovation Office; Department of Atomic Energy; Department of Science and Technology; ICSC —National Research Centre for High Performance Computing, Big Data and Quantum Computing, funded by the NextGenerationEU program; FAIR — Future Artificial Intelligence Research; funded by the NextGenerationEU program; Institute for Research in Fundamental Studies; Science Foundation; Istituto Nazionale di Fisica Nucleare; Korean Ministry of Education, Science and Technology; National Research Foundation of Korea; MES; Research Council of Lithuania (LMTLT), agreement No. VS-19; Ministry of Education; University of Malaya; BUAP; CINVESTAV; CONACYT; LNS; SEP; UASLP; MOS; Ministry of Business, Innovation and Employment; Pakistan Atomic Energy Commission; Ministry of Educaton and Science; National Science Centre; Fundação para a Ciência e a Tecnologia; Ministry of Education, Science and Technological Development of Serbia; MCIN/AEI; ERDF “a way of making Europe”; Fondo Europeo de Desarrollo Regional, Spain; Plan de Ciencia, Tecnología e Innovación del Principado de Asturias; MOSTR; ETH Board; ETH Zurich; PSI; SNF; UniZH; Canton Zurich; SER; Ministry of Higher Education, Science, Research and Innovation; National Science and Technology Development Agency of Thailand; Scientific and Technical Research Council of Turkey; Turkish Atomic Energy Authority; National Academy of Sciences of Ukraine; Science and Technology Facilities Council; US Department of Energy; US National Science Foundation; Marie-Curie programme; European Research Council and EPLANET; European Research Council/European Cooperation in Science and Technology; Horizon 2020 Grant; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Science Committee; Belgian Federal Science Policy Office; Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture; Belgian Fonds de la Recherche Scientifique, “Excellence of Science - EOS”; Belgian Fonds voor Wetenschappelijk Onderzoek, “Excellence of Science - EOS”; Beijing Municipal Science and Technology Commission; Fundamental Research Funds for the Central Universities; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy – EXC 2121 “Quantum Universe”; Hellenic Foundation for Research and Innovation; Hungarian Academy of Sciences; New National Excellence Program - ÚNKP, the NKFIH research grants; Council of Scientific and Industrial Research, India; Latvian Council of Science; Ministy of Education and Science; National Science Center; National Priorities Research Program by Qatar National Research Fund; Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu; Programa Severo Ochoa del Principado de Asturias; CUAASC; National Science, Research and Innovation Fund via the Program Management Unit for Human Resources and Institutional Development, Research and Innovation; Kavli Foundation; Nvidia Corporation; Welch Foundation; Weston Havens Foundation; Institut für Hochenergiephysik (HEPHY) using the Cloud Infrastructure Platform (CLIP), Vienna; Inter-University Institute for High Energies, Brussels; Université Catholique de Louvain, Louvain-la-Neuve; São Paulo Research and Analysis Center, São Paulo; Universidade do Estado do Rio de Janeiro, Rio de Janeiro; University of Sofia, Sofia; Institute of High Energy Physics of the Chinese Academy of Sciences, Beijing; National Institute of Chemical Physics and Biophysics, Tallinn; Helsinki Institute of Physics, Helsinki; Grille de Recherche d’Ile de France; Institut national de physique nucléaire et de physique des particules, IN2P3, Villeurbanne; Institut de recherche sur les lois fondamentales de l’Univers; CEA, Université Paris-Saclay, Gif-sur-Yvette, France; Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris; Institut Pluridisciplinaire Hubert Curien (IPHC), Strasbourg; Deutsches Elektronen-Synchrotron, Hamburg; Karlsruher Institut für Technologie, Karlsruhe; RWTH Aachen University, Aachen; University of Ioánnina, Ioánnina; Wigner Research Centre for Physics, Budapest; Tata Institute of Fundamental Research, Mumbai; INFN CNAF, Bologna; INFN Sezione di Bari, Università di Bari, Politecnico di Bari, Bari; INFN Sezione di Pisa, Università di Pisa, Scuola Normale Superiore di Pisa, Pisa; INFN Sezione di Roma, Sapienza Università di Roma, Rome; INFN Sezione di Trieste, Università di Trieste, Trieste; Laboratori Nazionali di Legnaro, Legnaro; Kyungpook National University, Daegu; National Centre for Physics, Quaid-I-Azam University, Islamabad; Akademickie Centrum Komputerowe Cyfronet AGH, Krakow; National Centre for Nuclear Research, Swierk; Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa; Institute for High Energy Physics of National Research Centre ‘Kurchatov Institute’, Protvino; Institute for Nuclear Research (INR) of the Russian Academy of Sciences, Troitsk; Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’, Moscow; Joint Institute for Nuclear Research, Dubna; Korea Institute of Science and Technology Information (KISTI), Daejeon; Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid; Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander; Port d’Informació Científica, Bellaterra; CERN, European Organization for Nuclear Research, Geneva; CSCS - Swiss National Supercomputing Centre, Lugano; National Center for High-performance Computing (NCHC), Hsinchu City; Middle East Technical University, Physics Department, Ankara; National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov; GridPP, Brunel University, Uxbridge; GridPP, Imperial College, London; GridPP, Rutherford Appleton Laboratory, Didcot; GridPP, University of Bristol, Bristol; GridPP, University of Glasgow, Glasgow; GridPP, University of Oxford, Oxford; California Institute of Technology, Pasadena; Fermi National Accelerator Laboratory, Batavia; Massachusetts Institute of Technology, Cambridge; National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility, Berkeley; Open Science Grid (OSG) Consortium; Pittsburgh Supercomputing Center (PSC), Pittsburgh; Purdue University, West Lafayette; San Diego Supercomputer Center (SDSC), La Jolla; Texas Advanced Computing Center (TACC), Austin; University of California, San Diego, La Jolla; University of Colorado Boulder, Boulder; University of Florida, Gainesville; University of Nebraska-Lincoln, Lincoln; University of Wisconsin - Madison, Madison; Vanderbilt University, Nashville
Source: Reports on Progress in Physics ; volume 88, issue 6, page 067802 ; ISSN 0034-4885 1361-6633
Publisher Information: IOP Publishing
Publication Year: 2025
Description: This paper presents a model-agnostic search for narrow resonances in the dijet final state in the mass range 1.8–6 TeV. The signal is assumed to produce jets with substructure atypical of jets initiated by light quarks or gluons, with minimal additional assumptions. Search regions are obtained by utilizing multivariate machine-learning methods to select jets with anomalous substructure. A collection of complementary anomaly detection methods—based on unsupervised, weakly supervised, and semisupervised algorithms—are used in order to maximize the sensitivity to unknown new physics signatures. These algorithms are applied to data corresponding to an integrated luminosity of 138 fb −1 , recorded by the CMS experiment at the LHC, at a center-of-mass energy of 13 TeV. No significant excesses above background expectations are seen. Exclusion limits are derived on the production cross section of benchmark signal models varying in resonance mass, jet mass, and jet substructure. Many of these signatures have not been previously sought, making several of the limits reported on the corresponding benchmark models the first ever. When compared to benchmark inclusive and substructure-based search strategies, the anomaly detection methods are found to significantly enhance the sensitivity to a variety of models.
Document Type: article in journal/newspaper
Language: unknown
DOI: 10.1088/1361-6633/add762
DOI: 10.1088/1361-6633/add762/pdf
Availability: https://doi.org/10.1088/1361-6633/add762; https://iopscience.iop.org/article/10.1088/1361-6633/add762; https://iopscience.iop.org/article/10.1088/1361-6633/add762/pdf
Rights: https://creativecommons.org/licenses/by/4.0/ ; https://iopscience.iop.org/info/page/text-and-data-mining
Accession Number: edsbas.823FF892
Database: BASE