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Record W2221053351 · doi:10.1103/physrevd.93.031301

Uncovering mass generation through Higgs flavor violation

2016· article· en· W2221053351 on OpenAlexaff
Wolfgang Altmannshofer, Stefania Gori, Alexander L. Kagan, L. Silvestrini, Jure Zupan

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsPerimeter Institute
FundersEuropean Research CouncilCold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of SciencesDeutsche ForschungsgemeinschaftU.S. Department of Energy
KeywordsHiggs bosonParticle physicsFlavorPhysicsMass generationChemistryNeutrinoFood science

Abstract

fetched live from OpenAlex

A discovery of the flavor-violating decay $h\ensuremath{\rightarrow}\ensuremath{\tau}\ensuremath{\mu}$ at the LHC would require extra sources of electroweak symmetry breaking (EWSB) beyond the Higgs in order to reconcile it with the bounds from $\ensuremath{\tau}\ensuremath{\rightarrow}\ensuremath{\mu}\ensuremath{\gamma}$, barring fine-tuned cancellations. In fact, an $h\ensuremath{\rightarrow}\ensuremath{\tau}\ensuremath{\mu}$ decay rate at a level indicated by the CMS measurement is easily realized if the muon and electron masses are due to a new source of EWSB, while the tau mass is due to the Higgs. We illustrate this with two examples: a two Higgs doublet model, and a model in which the Higgs is partially composite, with EWSB triggered by a technicolor sector. The first- and second-generation quark masses and Cabibbo-Kobayashi-Maskawa mixing can also be assigned to the new EWSB source. Large deviations in the flavor diagonal lepton and quark Higgs Yukawa couplings are generic. If ${m}_{\ensuremath{\mu}}$ is due to a rank 1 mass matrix contribution, a novel Yukawa coupling sum rule holds, providing a precision test of our framework. Flavor-violating quark and lepton (pseudo)scalar couplings combine to yield a sizable ${B}_{s}\ensuremath{\rightarrow}\ensuremath{\tau}\ensuremath{\mu}$ decay rate, which could be $\mathcal{O}(100)$ times larger than the Standard Model ${B}_{s}\ensuremath{\rightarrow}\ensuremath{\mu}\ensuremath{\mu}$ decay rate.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.403
Teacher spread0.381 · 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 designTheoretical or conceptual
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

Citations73
Published2016
Admission routes1
Has abstractyes

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