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Record W3104853802

Dark satellites and the morphology of dwarf galaxies

2012· article· en· W3104853802 on OpenAlexaff
Laura V. Sales, Else Starkenburg, Tjitske Starkenburg, Carlos Vera-Ciro

Bibliographic record

VenueMPG.PuRe (Max Planck Society) · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDwarf galaxy problemDwarf galaxyPhysicsAstrophysicsDwarf spheroidal galaxyGalaxyAstronomyLocal GroupLuminosityGalaxy formation and evolutionInteracting galaxy
DOInot available

Abstract

fetched live from OpenAlex

One of the strongest predictions of the LambdaCDM cosmological model is the presence of dark satellites orbiting all types of galaxies. We focus here on the dynamical effects of such satellites on disky dwarf galaxies, and demonstrate that these encounters can be dramatic. Although mergers with M_sat > M_d are not very common, because of the lower baryonic content they occur much more frequently on the dwarf scale than for L_*-galaxies. As an example, we present a numerical simulation of a 20% (virial) mass ratio merger between a dark satellite and a disky dwarf (akin to the Fornax dwarf galaxy in luminosity) that shows that the merger remnant has a spheroidal morphology. We conclude that perturbations by dark satellites provide a plausible path for the formation of dSph systems and also could trigger starbursts in gas rich dwarf galaxies. Therefore the transition from disky to the often amorphous, irregular, or spheroidal morphologies of dwarfs could be a natural consequence of the dynamical heating of hitherto unobservable dark satellites.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

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 studies0.0000.001
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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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 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

Citations27
Published2012
Admission routes1
Has abstractyes

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