{"id":"W4405250163","doi":"10.1140/epjc/s10052-025-14097-x","title":"Reweighting simulated events using machine-learning techniques in the CMS experiment","year":2025,"lang":"en","type":"article","venue":"The European Physical Journal C","topic":"Particle physics theoretical and experimental studies","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Particle Physics","funders":"Institut National de Physique Nucléaire et de Physique des Particules; Agencia Estatal de Investigación; Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture; Qatar National Research Fund; Ministry of Higher Education, Science, Research and Innovation, Thailand; Hellenic Foundation for Research and Innovation; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; Ministry of Science,Technology and Research; Latvijas Zinātnes Padome; European Regional Development Fund; Centre National de la Recherche Scientifique; Türkiye Enerji, Nükleer ve Maden Araştırma Kurumu; National Academy of Sciences of Ukraine; U.S. Department of Energy; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Ministry of Education, India; Benemérita Universidad Autónoma de Puebla; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; National Natural Science Foundation of China; National Science and Technology Development Agency; A.G. Leventis Foundation; Fundamental Research Funds for the Central Universities; Fonds Wetenschappelijk Onderzoek; Ministry of Science, ICT and Future Planning; Fonds De La Recherche Scientifique - FNRS; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Bundesministerium für Bildung und Forschung; Ministerio de Economía y Competitividad; Alexander von Humboldt-Stiftung; Secretaría de Educación Superior, Ciencia, Tecnología e Innovación; Consejo Nacional de Ciencia y Tecnología; National Research Centre; Austrian Science Fund; Ministerstvo Školství, Mládeže a Tělovýchovy; Fundação para a Ciência e a Tecnologia; Universidad Autónoma de San Luis Potosí; Kavli Foundation; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; European Commission; Fundação de Amparo à Pesquisa do Estado de São Paulo; Belgian Federal Science Policy Office; National Science Foundation; Science Foundation Ireland; Nvidia; Department of Science and Technology, Ministry of Science and Technology, India; Shota Rustaveli National Science Foundation; Deutsche Forschungsgemeinschaft; Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional; CERN; Science and Technology Facilities Council; Welch Foundation; Weston Havens Foundation; Bulgarian National Science Fund; Magyar Tudományos Akadémia; National Science Council; Pakistan Atomic Energy Commission; Fonds National de la Recherche Luxembourg; Alfred P. Sloan Foundation","keywords":"Large Hadron Collider; Detector; Event (particle physics); Set (abstract data type); Computer science; Luminosity; Event reconstruction; Particle physics; Discrete event simulation; Sample (material); Algorithm; Physics; Simulation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003051436,0.0006695858,0.0005778631,0.00056528,0.0005672919,0.0009324757,0.001597219,0.0008805442,0.00162718],"category_scores_gemma":[0.01117878,0.0004894324,0.0006526927,0.0008670366,0.0006216932,0.001204277,0.0007772275,0.001492851,0.0003798635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001110388,"about_ca_system_score_gemma":0.0008669879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004080763,"about_ca_topic_score_gemma":0.003950982,"domain_scores_codex":[0.9987549,0.0005529341,0.0000663331,0.000167319,0.0003785796,0.00007991203],"domain_scores_gemma":[0.9963008,0.002313616,0.0002738804,0.0005664563,0.0004319937,0.0001132923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004994544,0.0001482474,0.0056214,0.00005173323,0.00007809577,0.0001296019,0.0001011313,0.9374677,0.005183514,0.008470719,0.0009147434,0.04133367],"study_design_scores_gemma":[0.00002373487,0.00003630872,0.0005418914,0.000003884941,0.000007654176,0.00001784763,0.000008248465,0.9896607,0.005052224,0.004080431,0.0005551342,0.00001185469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3769094,0.0001890882,0.6136017,0.0004064423,0.0001577973,0.0001574954,0.0004579286,0.004701214,0.003418927],"genre_scores_gemma":[0.7924702,0.00007312561,0.2043562,0.0001822816,0.00003308702,0.0001372714,0.0008981082,0.0007147796,0.001135093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004080763,"threshold_uncertainty_score":0.01613772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0175006491302502,"score_gpt":0.3057803970763934,"score_spread":0.2882797479461432,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). 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