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Record W4223688201 · doi:10.1088/1361-6633/ac9cee

New physics searches at kaon and hyperon factories

2023· article· en· W4223688201 on OpenAlexaff
Goudzovski, Evgueni

Bibliographic record

VenueArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsPerimeter InstituteMcGill University
FundersHigh Energy PhysicsDivision of PhysicsNational Key Research and Development Program of ChinaScience and Technology Facilities CouncilJapan Society for the Promotion of ScienceHorizon 2020 Framework ProgrammeOffice of ScienceJavna Agencija za Raziskovalno Dejavnost RSChina Postdoctoral Science FoundationChinese Academy of SciencesH2020 Marie Skłodowska-Curie ActionsMinisterio de Economía y CompetitividadNational Natural Science Foundation of ChinaU.S. Department of EnergyEuropean CommissionLos Alamos National LaboratoryAlexander von Humboldt-StiftungEngineering and Physical Sciences Research CouncilUK Research and InnovationAlfred P. Sloan FoundationMinisterio de Ciencia e InnovaciónResearch Corporation for Science AdvancementNational Science Foundation
KeywordsPhysicsHyperonParticle physicsNuclear physicsHadron

Abstract

fetched live from OpenAlex

Rare meson decays are among the most sensitive probes of both heavy and light new physics. Among them, new physics searches using kaons benefit from their small total decay widths and the availability of very large datasets. On the other hand, useful complementary information is provided by hyperon decay measurements. We summarize the relevant phenomenological models and the status of the searches in a comprehensive list of kaon and hyperon decay channels. We identify new search strategies for under-explored signatures, and demonstrate that the improved sensitivities from current and next-generation experiments could lead to a qualitative leap in the exploration of light dark sectors.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.043
GPT teacher head0.228
Teacher spread0.185 · 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.

Study designNot applicable
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

Citations80
Published2023
Admission routes1
Has abstractyes

Explore more

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