The Glow of Axion Quark Nugget Dark Matter: (I) Large Scale Structures
Bibliographic record
Abstract
Axion quark nuggets (AQNs) are hypothetical objects with a mass greater than a few grams and sub-micrometer size, formed during the quark-hadron transition. Originating from the axion field, they offer a possible resolution of the similarity between visible and dark components of the Universe. These composite objects behave as cold dark matter, interacting with ordinary matter and resulting in pervasive electromagnetic radiation throughout the Universe. This work aims to predict the electromagnetic signature in large-scale structures from the AQN-baryon interaction, accounting for thermal and non-thermal radiations. We use Magneticum hydrodynamical simulations to describe the distribution and dynamics of gas and dark matter at cosmological scales. We calculate the electromagnetic signature from radio, starting at $ν\sim$ 1 GHz, up to a few keV X-ray energies. We find that the AQNs signature is characterized by monopole and fluctuation signals. The amplitude of both signals strongly depends on the average AQN mass and the ionization level of the baryonic environment. We identify a most optimistic scenario with a signal often near the sensitivity limit of existing instruments, such as FIRAS and the South Pole Telescope for high-resolution. Fluctuations in the Extra-galactic Background Light caused by the AQN can be tested with space-based imagers Euclid and James Webb Space Telescope. We also identify a minimal configuration, still out of reach of existing instruments, but future experiments might be able to pose constraints on the AQN model. We conclude that this is a viable dark matter model, which does not violate the canons of cosmology, nor existing observations. The best chances for testing this model reside in 1) ultra-deep IR and optical surveys, 2) spectral distorsions of the CMB and 3) low-frequency (1 GHz < $ν$ < 100 GHz) and high-resolution ($\ell > 10^4$) observations.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".