An implementation of neural simulation-based inference for parameter estimation in ATLAS

Neural simulation-based inference (NSBI) is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a NSBI framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses.

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Publication Details

Journal
Reports on Progress in Physics
Published
2025-05-02
DOI
https://doi.org/10.1088/1361-6633/add370
Citations
11
Primary Topic
Particle Detector Development and Performance
Type
article
Field-Weighted Citation Impact
20.57

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article

An implementation of neural simulation-based inference for parameter estimation in ATLAS

G. Navarro
11 citations
Reports on Progress in Physics
Particle Detector Development and Performance
20.57
article

An implementation of neural simulation-based inference for parameter estimation in ATLAS

G. Navarro
article en
11 citations

Abstract

Neural simulation-based inference (NSBI) is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a NSBI framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses.

Reports on Progress in PhysicsVol. 88(6)
European Organization for Nuclear Research (CH), University of Glasgow (GB)
National Science Foundation, U.S. Department of Energy, Alexander von Humboldt-Stiftung, Canarie, Baden-Württemberg Stiftung, TRIUMF, CERN, UK Research and Innovation, Centres de Recerca de Catalunya, Southern Methodist University, Türkiye Enerji, Nükleer ve Maden Araştırma Kurumu, Leverhulme Trust, Royal Society, European Commission, Deutsche Forschungsgemeinschaft, Agence Nationale de la Recherche, Ministry of Education, Culture, Sports, Science and Technology, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Danmarks Grundforskningsfond, Fundação de Amparo à Pesquisa do Estado de São Paulo, National Natural Science Foundation of China, Ministerstvo Školství, Mládeže a Tělovýchovy, Grantová Agentura České Republiky, Bundesministerium für Bildung und Forschung, Austrian Science Fund, Carl Tryggers Stiftelse för Vetenskaplig Forskning, Generalitat de Catalunya, Ministry of Science and Technology of the People's Republic of China, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Generalitat Valenciana, Bundesministerium für Wissenschaft, Forschung und Wirtschaft, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Knut och Alice Wallenbergs Stiftelse, Max-Planck-Gesellschaft, Vetenskapsrådet, Ministerstwo Edukacji i Nauki, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Centre National de la Recherche Scientifique, Ministerio de Ciencia e Innovación, Norges Forskningsråd, Centre National pour la Recherche Scientifique et Technique, Forskningsrådet om Hälsa, Arbetsliv och Välfärd, Istituto Nazionale di Fisica Nucleare, Narodowa Agencja Wymiany Akademickiej, Agencia Nacional de Investigación y Desarrollo, National Science and Technology Council, Ministero dell'Università e della Ricerca, University of Massachusetts Amherst, Horizon 2020 Framework Programme, Natural Sciences and Engineering Research Council of Canada, Science and Technology Facilities Council, Japan Society for the Promotion of Science, Fundação para a Ciência e a Tecnologia, Agencia Nacional de Promoción Científica y Tecnológica, British Columbia Knowledge Development Fund, European Regional Development Fund, Agencia Estatal de Investigación, Institut National de Physique Nucléaire et de Physique des Particules, European Social Fund, H2020 European Research Council, H2020 Marie Skłodowska-Curie Actions, CHIST-ERA
Openalex Percentile: Top 1%
Particle Detector Development and Performance
20.57
DOIOpen Access PDFOpenAlex
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