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Record W3092295515 · doi:10.1093/mnras/staa3716

Creating a galaxy lacking dark matter in a dark matter-dominated universe

2020· article· en· W3092295515 on OpenAlexafffund
Andrea V. Macciò, Daniel Huterer Prats, Keri L. Dixon, Tobias Buck, Stefan Waterval, Nikhil Arora, Stéphane Courteau, Xi Kang

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsQueen's University
FundersH2020 European Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsDark matterAstrophysicsDark galaxyMixed dark matterHot dark matterDark fluidAstronomyScalar field dark matterDark matter haloGalaxyUniverseWarm dark matterGalaxy rotation curveGalaxy formation and evolutionDark energyCosmologyHalo

Abstract

fetched live from OpenAlex

ABSTRACT We use hydrodynamical cosmological simulations to show that it is possible to create, via tidal interactions, galaxies lacking dark matter (DM) in a DM-dominated universe. We select dwarf galaxies from the NIHAO project, obtained in the standard cold dark matter model and use them as initial conditions for simulations of satellite–central interactions. After just one pericentric passage on an orbit with a strong radial component, NIHAO dwarf galaxies can lose up to 80 per cent of their DM content, but, most interestingly, their central (≈8 kpc) DM-to-stellar mass ratio changes from a value of ∼25, as expected from numerical simulations and abundance matching techniques, to roughly unity as reported for NGC 1052-DF2 and NGC 1054-DF4. The stellar velocity dispersion drops from ∼30 $\, \rm km\, s^{-1}$ before infall to values as low as 6 ± 2 $\, \rm km\, s^{-1}$. These, and the half-light radius around 3 kpc, are in good agreement with observations from van Dokkum and collaborators. Our study shows that it is possible to create a galaxy without DM starting from typical dwarf galaxies formed in a DM-dominated universe, provided they live in a dense environment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.001
Research integrity0.0000.000
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 head0.008
GPT teacher head0.201
Teacher spread0.194 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations41
Published2020
Admission routes2
Has abstractyes

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