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Record W2905219084 · doi:10.22215/etd/2016-11537

Dark Matter and Collider Phenomenology of Large Electroweak Scalar Multiplets

2016· dissertation· en· W2905219084 on OpenAlexaff
Terry Pilkington

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsCongress of Aboriginal Peoples
Fundersnot available
KeywordsPhysicsElectroweak interactionParticle physicsHyperchargePhysics beyond the Standard ModelDark matterHiggs bosonScalar (mathematics)Parameter spaceLarge Hadron ColliderPhenomenology (philosophy)SupersymmetryStandard Model (mathematical formulation)Statistics

Abstract

fetched live from OpenAlex

The Universe must contain some form of dark matter (DM), based on many astrophysical and cosmological observations. The Standard Model of particle physics (SM) does not contain a DM candidate. We may extend the SM by the addition of a single large electroweak complex scalar multiplet. These multiplets may have at most 8 members. Based on the isospin and hypercharge assignments, and the form of the scalar potential, the models we examine in this thesis have either 6 or 8 members. One of the newly-introduced particles is a DM candidate. We use the following theoretical considerations and experimental results to constrain the parameter space of these models: perturbative unitarity of scattering; stability of the scalar potential; direct searches for new physics at the LHC; electroweak observables (STU); and decays of the Higgs boson. We then compute the relic abundance in the models to determine the viability of our DM candidate. Finally, we examine the prospects of discovery at direct detection experiments. We find that these models may form part of the total DM content in the mass range 80-1000 GeV, and will begin to be probed in the direct detection experiments currently under construction. For the mass range > 5 TeV, we must also consider the effects of co-annihilation and Sommerfeld enhancement. In this range, our DM candidate may be all of the DM, but will require a subsequent generation of direct detection experiments to probe its parameter space.

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.258
Teacher spread0.254 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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