DAMA/LIBRA annual modulation and axion quark nugget dark matter model
Bibliographic record
Abstract
The DAMA/LIBRA (DL) experiment shows $9.5\ensuremath{\sigma}$ evidence for an annual modulation in the (1--6) keV energy range, strongly suggesting that the observed modulation has a dark matter origin. However, the conventional interpretation in terms of weakly interacting massive particle--nucleon interaction is excluded by other experiments. We propose an alternative source of modulation in the form of neutrons, which have been liberated from surrounding material. Our computations are based on the so-called axion quark nugget (AQN) dark matter model, which was originally invented long ago to explain the similarity between the dark and visible cosmological matter densities, i.e., ${\mathrm{\ensuremath{\Omega}}}_{\mathrm{dark}}\ensuremath{\sim}{\mathrm{\ensuremath{\Omega}}}_{\text{visible}}$. In our proposal, the annual modulation is shown to be generated in the keV energy range, which is consistent with the DL observation in (1--6) keV range. This keV energy scale in our proposal is mostly determined by spectral properties of the neutrinos emitted by the AQN dark matter particles, while the absence of the modulation with energies above 6 keV is explained by a sharp cutoff in the neutrino's energy spectrum at $\ensuremath{\sim}15\text{ }\text{ }\mathrm{MeV}$. This proposal can be directly tested by COSINE-100, ANAIS-112, CYGNO, and other similar experiments. It can be also tested by studying the correlations between the signals from these experiments and the signatures from drastically different detectors designed for studies of infrasonic or seismic events using such instruments as distributed acoustic sensing.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".