Maximal axion misalignment from a minimal model
Bibliographic record
Abstract
Abstract The QCD axion is one of the best motivated dark matter candidates. The misalignment mechanism is well known to produce an abundance of the QCD axion consistent with dark matter for an axion decay constant of order 1012 GeV. For a smaller decay constant, the QCD axion, with Peccei-Quinn symmetry broken during inflation, makes up only a fraction of dark matter unless the axion field starts oscillating very close to the top of its potential, in a scenario called “large-misalignment”. In this scenario, QCD axion dark matter with a small axion decay constant is partially comprised of very dense structures. We present a simple dynamical model realising the large-misalignment mechanism. During inflation, the axion classically rolls down its potential approaching its minimum. After inflation, the Universe reheats to a high temperature and a modulus (real scalar field) changes the sign of its minimum dynamically, which changes the sign of the mass of a vector-like fermion charged under QCD. As a result, the minimum of the axion potential during inflation becomes the maximum of the potential after the Universe has cooled through the QCD phase transition and the axion starts oscillating. In this model, we can produce QCD axion dark matter with a decay constant as low as 6 × 109 GeV and an axion mass up to 1 meV. We also summarise the phenomenological implications of this mechanism for dark matter experiments and colliders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".