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Record W3103351472

Singlet Neighbors of the Higgs Boson

2016· article· en· W3103351472 on OpenAlexaff
Brian Batell, David McKeen, Maxim Pospelov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsParticle physicsHiggs bosonPhysicsLarge Hadron ColliderLeptonSinglet stateFermionNuclear physicsBosonStandard Model (mathematical formulation)Higgs fieldPhysics beyond the Standard Model
DOInot available

Abstract

fetched live from OpenAlex

The newly discovered resonance at 125 GeV has properties consistent with the Standard Model (SM) Higgs particle, although some production and/or decay channels currently exhibit O(1) deviations. We consider scenarios with a new scalar singlet field with couplings to electrically charged vector-like matter, focusing particularly on the case when the singlet mass lies within a narrow ~ few GeV window around the Higgs mass. Such a 'singlet neighbor' presents novel mechanisms for modifying the observed properties of the Higgs boson. For instance, even a small amount of the Higgs-singlet mixing can lead to a significant enhancement of the apparent diphoton rate. Alternatively, the Higgs may decay into the nearby singlet, along with a very light, very soft mediator particle, in which case there can be O(1) enhancement to the apparent diphoton rate even for ~ TeV-scale charged vector-like matter. We also explore models in which vector-like fermions mix with the SM leptons, causing the simultaneous enhancement of γγ and suppression of $ \tau \overline{\tau } $ Higgs branching ratios. Our scenario can be tested with the accumulating LHC data by probing for the di-resonance structure of the 125 GeV diphoton signal, as well as the relative shift in the resonance location between the diphoton and four-lepton modes.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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)0.0040.000

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 head0.009
GPT teacher head0.234
Teacher spread0.224 · 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

Citations32
Published2016
Admission routes1
Has abstractyes

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