{"id":"W7113134864","doi":"","title":"Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network","year":2025,"lang":"en","type":"article","venue":"Lancaster EPrints (Lancaster University)","topic":"Particle physics theoretical and experimental studies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 Marie Skłodowska-Curie Actions; Institut National de Physique Nucléaire et de Physique des Particules; Agencia Estatal de Investigación; Agencia Nacional de Promoción Científica y Tecnológica; Fundação para a Ciência e a Tecnologia; Ministry of Education, Culture, Sports, Science and Technology; Bundesministerium für Bildung und Forschung; Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Science and Technology Facilities Council; Horizon 2020 Framework Programme; Narodowa Agencja Wymiany Akademickiej; Forskningsrådet om Hälsa, Arbetsliv och Välfärd; Ministerstvo Školství, Mládeže a Tělovýchovy; National Science and Technology Council; European Social Fund; Royal Society; Centre National pour la Recherche Scientifique et Technique; European Regional Development Fund; British Columbia Knowledge Development Fund; Max-Planck-Gesellschaft; Centre National de la Recherche Scientifique; U.S. Department of Energy; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Japan Society for the Promotion of Science; Ministerstwo Edukacji i Nauki; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Bundesministerium für Wissenschaft, Forschung und Wirtschaft; Generalitat de Catalunya; Generalitat Valenciana; Agencia Nacional de Investigación y Desarrollo; UK Research and Innovation; Istituto Nazionale di Fisica Nucleare; Ministero dell'Università e della Ricerca; Grantová Agentura České Republiky; Austrian Science Fund; National Natural Science Foundation of China; European Commission; Leverhulme Trust; Fundação de Amparo à Pesquisa do Estado de São Paulo; Deutsche Forschungsgemeinschaft; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Ministry of Science and Technology of the People's Republic of China; Agence Nationale de la Recherche; National Science Foundation; Baden-Württemberg Stiftung; H2020 European Research Council; Norges Forskningsråd; Alexander von Humboldt-Stiftung; TRIUMF; Danmarks Grundforskningsfond; Türkiye Enerji, Nükleer ve Maden Araştırma Kurumu; Canarie; CERN; Centres de Recerca de Catalunya","keywords":"Calibration; Atlas (anatomy); Artificial neural network; Estimator; Context (archaeology); Large Hadron Collider; Deep neural networks; Calorimeter (particle physics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002820164,0.0006374615,0.0005140278,0.0005812498,0.0005701621,0.001120509,0.00107593,0.0009270815,0.001058642],"category_scores_gemma":[0.006431066,0.0004008412,0.0003314018,0.0009831608,0.0008327354,0.001218943,0.001664943,0.001509909,0.0002290821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001727085,"about_ca_system_score_gemma":0.0009427309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007093066,"about_ca_topic_score_gemma":0.01191694,"domain_scores_codex":[0.9989163,0.0003398876,0.00003432753,0.0002613198,0.000372993,0.00007532172],"domain_scores_gemma":[0.9986792,0.0005847476,0.000140434,0.0002085553,0.0003359307,0.00005099226],"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":[0.0008869383,0.0000865255,0.006911842,0.00008657428,0.0001294533,0.0000605512,0.00008720529,0.8997557,0.01430551,0.01190038,0.001261892,0.06452746],"study_design_scores_gemma":[0.00002274558,0.00003474936,0.002119619,0.00001100917,0.00001523041,0.00001701278,0.00001044692,0.982074,0.01127911,0.00391508,0.0004739414,0.00002708464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4453897,0.0005315596,0.5400881,0.00096521,0.0001799708,0.00005385871,0.0005555542,0.002228939,0.01000704],"genre_scores_gemma":[0.946553,0.00006744069,0.05162705,0.000100162,0.00002010697,0.00002324495,0.0003307965,0.000126549,0.00115177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007093066,"threshold_uncertainty_score":0.01491463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02794430468540106,"score_gpt":0.2724551571100539,"score_spread":0.2445108524246528,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). 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