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Record W2904663425 · doi:10.1007/jhep01(2019)216

Electroweak phase transition and Higgs phenomenology in the Georgi-Machacek model

2019· article· en· W2904663425 on OpenAlexafffund
Ruiyu Zhou, W. S. Cheng, Xin Deng, Ligong Bian, Yongcheng Wu

Bibliographic record

VenueJournal of High Energy Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Science and ICT, South KoreaMinistry of Education, Science and TechnologyNational Research Foundation of KoreaNational Natural Science Foundation of ChinaNational Research Foundation
KeywordsParticle physicsElectroweak interactionHiggs bosonPhysicsLarge Hadron ColliderPhenomenology (philosophy)IsospinPhase transitionVacuum expectation valueNuclear physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract In this work, we perform the electroweak phase transition study with the Georgi-Machacek model. We investigate both the one-step and two-step strong first order electroweak phase transition (SFOEWPT). The SFOEWPT viable parameter spaces could be tested by the future 14TeV LHC, HL-LHC, and ILC. The LHC Higgs signal strength measurements severely bound the SFOEWPT valid parameter spaces, a tinny region of the mixing angle between the neutral fields of the isospin-doublet and isospin-triplet scalars around α ∼ 0 can allow the two-step SFOEWPT to occur. The triplet vacuum expectation value (VEV) is crucial for both SFOEWPT and related Higgs phenomenology. The two-step SFOEWPT can be distinguished from the one-step SFOEWPT through the triple Higgs coupling searches and the low mass doubly charged Higgs searches at colliders.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.241
Teacher spread0.234 · 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

Citations45
Published2019
Admission routes2
Has abstractyes

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