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Record W7113134864

Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network

2025· article· en· W7113134864 on OpenAlexfundno aff

Bibliographic record

VenueLancaster EPrints (Lancaster University) · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsnot available
FundersH2020 Marie Skłodowska-Curie ActionsInstitut National de Physique Nucléaire et de Physique des ParticulesAgencia Estatal de InvestigaciónAgencia Nacional de Promoción Científica y TecnológicaFundação para a Ciência e a TecnologiaMinistry of Education, Culture, Sports, Science and TechnologyBundesministerium für Bildung und ForschungNatural Sciences and Engineering Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungScience and Technology Facilities CouncilHorizon 2020 Framework ProgrammeNarodowa 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 EnergyFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroJapan Society for the Promotion of ScienceMinisterstwo 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 FundNational Natural Science Foundation of ChinaEuropean CommissionLeverhulme TrustFundação de Amparo à Pesquisa do Estado de São PauloDeutsche 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 KurumuCanarieCERNCentres de Recerca de Catalunya
KeywordsCalibrationAtlas (anatomy)Artificial neural networkEstimatorContext (archaeology)Large Hadron ColliderDeep neural networksCalorimeter (particle physics)
DOInot available

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.272
Teacher spread0.245 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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