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Record W4403670998 · doi:10.1088/1402-4896/ad8aa5

Effect of Einasto spike on the gravitationally decoupled self-gravitating dark matter halos

2024· article· en· W4403670998 on OpenAlexaff
Z. Yousaf, T. Ganesan, Bander Almutairi, M. Z. Bhatti, S. Khan

Bibliographic record

VenuePhysica Scripta · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsUniversity of Calgary
FundersDeanship of Scientific Research, King Saud University
KeywordsPhysicsDark matterSpike (software development)HaloAstrophysicsAstronomyGalaxy

Abstract

fetched live from OpenAlex

Abstract In this work, we consider the possibility of constructing gravitationally bound, charged dark matter halos motivated by Einasto density model. This model generalizes the concept of charged, non-commutative mini-compact objects by including dark matter as its primary component through the principles of the minimal geometric deformation strategy. We point out that the coupling of the non-commutativity inspired Einasto spike density model with a non-isotropic fluid, within the context of gravitational decoupling allows the formation of different minimally deformed dark matter halos, corresponding to each value of the deformation parameter. We assume the Tolman-Kuchowicz cosmological model as a seed metric within the geometric deformation scheme to initially generate an electrically charged isotropic solution. Subsequently, we employ a density-like constraint to produce a second anisotropic charged dark matter stellar model via the Einasto density profile. A complete graphical analysis of the structural variables and stability of both models indicate that, for the considered choice of parameters, both cosmological models are well-behaved, exhibiting expected physical behavior.

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)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.256
Teacher spread0.251 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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