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Searches for lepton number violating K+ decays

2019· article· en· W2954024284 on OpenAlexafffund
E. Cortina, Toshio Numao, Y. Petrov, Michal Zamkovsky, A. Antonelli, G. Lanfranchi, Giampaolo Mannocchi, S. Martellotti, Matthew Moulson, Italo Mannelli, C. Graham, E. Maurice, B. Wrona, A. Conovaloff, Peter Cooper, D. Coward, Philip David Rubin

Bibliographic record

VenuePhysics Letters B · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsUniversity of British ColumbiaTRIUMF
FundersNatural Sciences and Engineering Research Council of CanadaJoint Institute for Nuclear ResearchMinistry of Education and Science of the Russian FederationScience and Technology Facilities CouncilNational Research Council CanadaMinistero dell’Istruzione, dell’Università e della RicercaMinistry of Education, Youth and ScienceMinisterstvo školstva, vedy, výskumu a športu Slovenskej republikyEuropean Research CouncilFonds De La Recherche Scientifique - FNRSBundesministerium für Bildung und ForschungRussian Academy of SciencesRussian Foundation for Basic ResearchMinisterstvo Školství, Mládeže a TělovýchovyConsejo Nacional de Ciencia y TecnologíaAgence Nationale de la RechercheNational Science FoundationTRIUMFRoyal SocietyIstituto Nazionale di Fisica NucleareCERNUniverzita Karlova v Praze
KeywordsLeptonParticle physicsPhysicsLarge Hadron ColliderLepton numberBranching fractionNuclear physicsBranching (polymer chemistry)ChemistryElectron

Abstract

fetched live from OpenAlex

The NA62 experiment at CERN reports a search for the lepton number violating decays K + → π − e + e + and K + → π − μ + μ + using a data sample collected in 2017. No signals are observed, and upper limits on the branching fractions of these decays of 2.2 × 10 − 10 and 4.2 × 10 − 11 are obtained, respectively, at 90% confidence level. These upper limits improve on previously reported measurements by factors of 3 and 2, respectively.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.020
GPT teacher head0.283
Teacher spread0.263 · 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 teacher head, 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

Citations46
Published2019
Admission routes2
Has abstractyes

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