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Record W4213416365 · doi:10.1139/cjp-2021-0332

Off-centering of the disk in the halo potential and the kinematic lopsidedness in the dwarf irregular galaxy WLM

2022· article· en· W4213416365 on OpenAlexvenueno aff
Maryam Khademi, S. Nasiri

Bibliographic record

VenueCanadian Journal of Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsGalaxy rotation curveDark matter haloAstrophysicsHaloDark matterGravitational potentialGalaxyPerturbation (astronomy)Galactic haloAstronomy

Abstract

fetched live from OpenAlex

In this work, to investigate the dynamical nature of the kinematic asymmetry in the isolated gas-rich dwarf irregular galaxy W LM in the Local Group, we consider that the dark matter halo and the disk do not have the same center of mass (i.e., the disk lies off-center in the potential of the extended dark matter halo), which is one of the possible physical explanations for the kinematic lopsidedness. To do so, we generate a lopsided halo potential by considering two dark matter mass density models, ISO and Burkert, and we add up the contribution to the rotation curve of a perturbation term [Formula: see text] in the gravitational potential, which arises from the offset between the disk and the dark matter halo. We show that such an m = 1 perturbation improves the rotation curve modeling when compared to a non-perturbed potential and the shape of the HI gas rotation curves is fitted better in the approaching side if the perturbation term in the halo potential is taken into account for this galaxy dynamics. In fact, displacing the disk center by 0.1 kpc from the halo center is sufficient to provide such an improvement in modeling the rotation curve.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.006
GPT teacher head0.181
Teacher spread0.175 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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