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Record W4390091569 · doi:10.48550/arxiv.2312.12500

Neutron Stars as the Dark Matter detectors

2023· preprint· en· W4390091569 on OpenAlexafffund
Ariel Zhitnitsky

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsAxionDark matterParticle physicsNeutron starStrange matterAstrophysicsObservableStarsLight dark matterQuarkNuclear physicsScalar field dark matterCosmologyQuantum mechanics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.004

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.

Opus teacher head0.067
GPT teacher head0.218
Teacher spread0.151 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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