Bibliographic record
Abstract
It has been known for quite sometime that the Neutron Stars (NS) can play a role of the Dark Matter (DM) detectors due to many uniques features of NS. We apply these (previously developed) ideas to a specific form of the DM when it is represented by a composite object, rather than by a local fundamental field (such as WIMPs). To be more precise we consider the so-called axion quark nuggets (AQN) dark matter model, when the ``non-baryonic" dark matter in fact is made of quarks and gluons which are in dense quark phase (similar to the old idea of the Witten's strangelets). We argue that the interaction of the AQNs with NS material may lead to many profound observable effects, which dramatically different from conventional picture when DM particles are represented by weakly interacting WIMPs. In particular, we argue that the AQNs may serve as the triggers for the magnetic reconnection to heat the NS surface. This effect may strongly alleviate (or even completely remove) the observed inconsistencies between the predicted and observed surface temperatures for many old NS. This heating mechanism is always accompanied by the hard X ray emission, which may serve as an indicator of the proposed mechanism.
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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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".