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Record W7052516236

Search for lepton-flavor-violating $\tau^- \to \mu^-\mu^+\mu^-$ decays at Belle II

2024· article· en· W7052516236 on OpenAlexfundno aff

Bibliographic record

VenueDiRROS repository (University of Maribor) · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsnot available
FundersInstitut National de Physique Nucléaire et de Physique des ParticulesNatural Sciences and Engineering Research Council of CanadaBundesministerium für Bildung, Wissenschaft und ForschungCentre National de la Recherche ScientifiqueCanarieBundesministerium für Bildung und ForschungNatural Science Foundation of Shandong ProvinceGrantová Agentura České RepublikyAustrian Science FundAgence Nationale de la RechercheMax-Planck-GesellschaftDeutsche ForschungsgemeinschaftNational Natural Science Foundation of ChinaCompute CanadaAlexander von Humboldt-Stiftung
KeywordsBranching fractionLimit (mathematics)Upper and lower boundsSelection (genetic algorithm)Set (abstract data type)Particle decayElectron–positron annihilationTree (set theory)
DOInot available

Abstract

fetched live from OpenAlex

We present the result of a search for the charged-lepton-flavor violating decay $\tau^- \to \mu^-\mu^+\mu^-$ using a $424fb^{-1}$ sample of data recorded by the Belle II experiment at the SuperKEKB $e^{-}e^{+}$ collider. The selection of $e^{-}e^{+}\to\tau^+\tau^-$ events is based on an inclusive reconstruction of the non-signal tau decay, and on a boosted decision tree to suppress background. We observe one signal candidate, which is compatible with the expectation from background processes. We set a $90\%$ confidence level upper limit of $1.9 \times 10^{-8}$ on the branching fraction of the \taumu decay, which is the most stringent bound to date.

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 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)0.002
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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
Published2024
Admission routes1
Has abstractyes

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