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Record W7132152471

Improved reconstruction of highly boosted ▫$tau$▫-lepton pairs in the ▫$tautau to (munu_{mu}nu_{tau})$▫(hadrons + ▫$nu_{tau}$▫) decay channels with the ATLAS detector

2025· article· en· W7132152471 on OpenAlexfundno aff
Georges Aad, Vladimir Cindro, Andrej Filipčič, Andrej Gorišek, Bojan Hiti, Borut Paul Kerševan, Gregor Kramberger, Blaz Leban, Boštjan Maček, Igor Mandić, Miha Mali, Marko Mikuž, Miha Muškinja, Jakob Novak, Tadej Novak, Jan Gavranovic, Jernej Debevc, Lukasz Zwalinski

Bibliographic record

VenueRUNG · 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 TecnologiaJapan Society for the Promotion of ScienceNarodowa 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 PauloJavna Agencija za Raziskovalno Dejavnost RSScience 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 KurumuCanarieCERNCentres de Recerca de CatalunyaMinisterio de Ciencia e Innovación
KeywordsDetectorChannel (broadcasting)Noise (video)Atlas (anatomy)
DOInot available

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.007
GPT teacher head0.235
Teacher spread0.228 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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 abstractno

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