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Anisotropic magnetized neutron star

2022· article· en· W4210676382 on OpenAlexaff
G. H. Bordbar, Mohammad Hossein Karami

Bibliographic record

VenueThe European Physical Journal C · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsUniversity of Waterloo
FundersUniversity of MazandaranShiraz University
KeywordsNeutron starPhysicsSchwarzschild radiusMagnetic fieldAnisotropyRADIUSEquation of stateScalar fieldScalar (mathematics)RedshiftExotic starGravitationAstrophysicsQuantum electrodynamicsClassical mechanicsQuantum mechanicsQuark starGeometryStrange matter

Abstract

fetched live from OpenAlex

Abstract As we know, the effect of strong magnetic field causes the anisotropy for the magnetized compact objects. Therefore, in this paper, we have studied the structure properties of anisotropic case of magnetized neutron star. We have derived the equation of state (EoS) of neutron star matter for two forms of magnetic fields, one uniform and one density dependent. We have solved the generalized Tolman–Oppenheimer–Volkoff equations to examine the maximum mass and corresponding radius, Schwarzschild radius, gravitational redshift, Kretschmann scalar, and Buchdahl theorem for this system. It was shown that the maximum mass and radius of neutron star are increasing functions of the magnetic field. Also redshift, strength of gravity, and Kretschmann scalar increase as the magnetic field increases. In addition, the dynamical stability of anisotrop neutron star has been investigated, and finally a comparison with the empirical results has been made.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.296
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations26
Published2022
Admission routes1
Has abstractyes

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