Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.
VenueWhite Rose Research Online (University of Leeds, The University of Sheffield, University of York) · 2024
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
FundersCHIST-ERAH2020 Marie Skłodowska-Curie ActionsInstitut National de Physique Nucléaire et de Physique des ParticulesAgencia Nacional de Promoción Científica y TecnológicaFundação para a Ciência e a TecnologiaJapan Society for the Promotion of ScienceAgencia Estatal de InvestigaciónUniversity of Massachusetts AmherstNarodowa Agencja Wymiany AkademickiejForskningsrådet om Hälsa, Arbetsliv och VälfärdMinisterstvo Školství, Mládeže a TělovýchovyNational Science and Technology CouncilEuropean Social FundRoyal SocietyCentre National pour la Recherche Scientifique et TechniqueEuropean Regional Development FundBritish Columbia Knowledge Development FundMax-Planck-GesellschaftCentre National de la Recherche ScientifiqueU.S. Department of EnergyCarl Tryggers Stiftelse för Vetenskaplig ForskningFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroKnut och Alice Wallenbergs StiftelseMinisterstwo Edukacji i NaukiConselho Nacional de Desenvolvimento Científico e TecnológicoBundesministerium für Wissenschaft, Forschung und WirtschaftGeneralitat de CatalunyaGeneralitat ValencianaAgencia Nacional de Investigación y DesarrolloUK Research and InnovationIstituto Nazionale di Fisica NucleareMinistero dell'Università e della RicercaGrantová Agentura České RepublikyAustrian Science FundNatural Sciences and Engineering Research Council of CanadaMinistry of Education, Culture, Sports, Science and TechnologyBundesministerium für Bildung und ForschungHorizon 2020 Framework ProgrammeVetenskapsrådetNational Natural Science Foundation of ChinaEuropean CommissionLeverhulme TrustFundação de Amparo à Pesquisa do Estado de São PauloScience and Technology Facilities CouncilSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungDeutsche ForschungsgemeinschaftNederlandse Organisatie voor Wetenschappelijk OnderzoekMinistry of Science and Technology of the People's Republic of ChinaAgence Nationale de la RechercheNational Science FoundationBaden-Württemberg StiftungH2020 European Research CouncilNorges ForskningsrådAlexander von Humboldt-StiftungTRIUMFDanmarks GrundforskningsfondTürkiye Enerji, Nükleer ve Maden Araştırma KurumuSouthern Methodist UniversityCanarieCERNCentres de Recerca de CatalunyaMinisterio de Ciencia e Innovación
KeywordsInferenceComputer scienceLarge Hadron ColliderStatistical inferenceArtificial neural networkRobustness (evolution)Higgs bosonParameter spaceAlgorithmHistogramConvolutional neural networkParticle physicsData miningArtificial intelligenceMathematicsStatisticsPhysics
Abstract
fetched live from OpenAlexNeural simulation-based inference 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 neural simulation-based inference 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.995
Codex and Gemma teacher scores by category
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
