Majorana dark matter through the Higgs portal under the vacuum stability lamppost
Bibliographic record
Abstract
We study the vacuum stability of a minimal Higgs portal model in which the standard model (SM) particle spectrum is extended to include one complex scalar field and one Dirac fermion. These new fields are singlets under the SM gauge group and are charged under a global $U(1)$ symmetry. Breaking of this $U(1)$ symmetry results in a massless Goldstone boson, a massive $CP$-even scalar, and splits the Dirac fermion into two new mass eigenstates, corresponding to Majorana fermions. The lightest Majorana fermion $(w)$ is absolutely stable, providing a plausible dark matter (DM) candidate. We show that interactions between the Higgs sector and the lightest Majorana fermion which are strong enough to yield a thermal relic abundance consistent with observation can easily destabilize the electroweak vacuum or drive the theory into a nonperturbative regime at an energy scale well below the Planck mass. However, we also demonstrate that there is a region of the parameter space which develops a stable vacuum (up to the Planck scale), satisfies the relic abundance, and is in agreement with direct DM searches. Such an interesting region of the parameter space corresponds to DM masses $350\text{ }\text{ }\mathrm{GeV}\ensuremath{\lesssim}{m}_{w}\ensuremath{\lesssim}1\text{ }\text{ }\mathrm{TeV}$. The region of interest is within reach of second-generation DM direct detection experiments.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".