The Higgs Triplet Model: mixing in the neutral sector, \nvector-like fermions, and dark matter
Bibliographic record
Abstract
The inability to predict neutrino masses and the existence of Dark Matter (DM) are two essential shortcomings of the Standard Model. This thesis is a phenomenological study of a Beyond the Standard Model (BSM) scenario, Higgs Triplet Model (HTM) and effects of introducing vector-like leptons and quarks on the Higgs sector in the model. The HiggsTriplet Model provides an elegant resolution to the first problem via the seesaw mechanism. Introducing a full representation of vector-like leptons can solve the DM problem of the HTM by assigning a vector-like neutrino to be the DM candidate. As a result, two important problems in SM will be resolved. \n \nWithin this context, after a review of the main concepts, we first revisit the neutral Higgs sector of the Higgs Triplet Model. We show that, under general considerations, an unmixed neutral Higgs boson cannot have an enhanced decay branching ratio into γγ with respect to the Standard Model one, while an enhancement is possible for the mixed case, but only for the heavier of the two neutral Higgs bosons. We then analyze the implications of introducing \nvector-like leptons in the Higgs Triplet Model. We show that, if the vector-like leptons are allowed to be relatively light, they enhance or suppress the decay rates of loop-dominated neutral Higgs bosons decays H → γγ and H → Zγ, as well as alter the decay patterns of the doubly-charged Higgs bosons, modifying the restrictions on their masses. We also look at the effects of vector-like quarks (singlets, doublets or triplets) in the Higgs Triplet Model. \nThough the new Higgs in the model couple to leptons only, some vector-like quarks help improve electroweak precision measurements and thus lift the mass of the doubly charged Higgs boson to within experimental limits. We also study the effects of introducing the vector-like quarks on the loop-dominated neutral Higgs decays. \n \nWe show that introducing vector-like leptons in the model also provides a resolution to the problem of Dark Matter. We investigate the invisible decay width of the Higgs boson and the electroweak precision variables, and impose restrictions on model parameters. We analyze the relic density constraint and calculate the cross sections for indirect and direct dark matter detection. With appropriate parameter restrictions, the Higgs Triplet Model with vector-like \nfermions is rendered completely consistent with the data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".