Axion quark nuggets and how a global network can discover them
Bibliographic record
Abstract
We advocate an idea that the presence of the daily and annual modulations of the axion flux on the Earth's surface may dramatically change the strategy of the axion searches. Our computations are based on the so-called axion quark nugget (AQN) dark-matter model which was originally put forward to explain the similarity of the dark and visible cosmological matter densities ${\mathrm{\ensuremath{\Omega}}}_{\mathrm{dark}}\ensuremath{\sim}{\mathrm{\ensuremath{\Omega}}}_{\text{visible}}$. In our framework, the population of galactic axions with mass ${10}^{\ensuremath{-}6}\text{ }\text{ }\mathrm{eV}\ensuremath{\lesssim}{m}_{a}\ensuremath{\lesssim}{10}^{\ensuremath{-}3}\text{ }\text{ }\mathrm{eV}$ and velocity $⟨{v}_{a}⟩\ensuremath{\sim}{10}^{\ensuremath{-}3}c$ will be always accompanied by the axions with typical velocities $⟨{v}_{a}⟩\ensuremath{\sim}0.6c$ emitted by AQNs. We formulate the broadband detection strategy to search for such relativistic axions by studying the daily and annual modulations. We describe several tests which could effectively discriminate a true signal from noise. These AQN-originated axions can be observed as correlated events which could be recorded by synchronized stations in the global network. The correlations can be effectively studied if the detectors are positioned at distances shorter than a few hundred kilometers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".