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Record W3110262325 · doi:10.1103/physrevd.102.123002

Dark kinetic heating of neutron stars from contact interactions with relativistic targets

2020· article· en· W3110262325 on OpenAlexafffund
A. Joglekar, Nirmal Raj, Philip Tañedo, Hai-Bo Yu

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsTRIUMF
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaAspen Center for PhysicsTRIUMFU.S. Department of EnergyNational Science Foundation
KeywordsPhysicsDark matterFermionPauli exclusion principleNeutron starSpin (aerodynamics)Particle physicsScatteringDegenerate energy levelsAstrophysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Dark matter can capture in neutron stars from scattering off ultrarelativistic electrons. We present a method to calculate the capture rate on degenerate targets with ultrarelativistic momenta in a compact astronomical object. Our treatment accounts for the target momentum and the Fermi degeneracy of the system. We derive scaling relations for scattering with relativistic targets and confirm consistency with the nonrelativistic limit and Lorentz invariance. The potential observation of kinetic heating of neutron stars has a larger discovery reach for dark matter--lepton interactions than conventional terrestrial direct detection experiments. We map this reach onto a set of bosonic and fermionic effective contact interactions between dark matter and leptons as well as nucleons. We show the results for the contact operators up to dimension-six for spin-0 and spin-$1/2$ dark matter interactions with relativistic as well as nonrelativistic Standard Model fermions. Highlights of this program in the case of vector mediated interactions are presented in a companion article [Joglekar et al., Phys. Lett. B 809, 135767 (2020)]. Our method is generalizable to dark matter scattering in any degenerate medium where the Pauli exclusion principle leads to relativistic targets with a constrained phase space for scattering.

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)
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 score0.517
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.357
Teacher spread0.342 · 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

Citations84
Published2020
Admission routes2
Has abstractyes

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