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Record W2380262006 · doi:10.1093/mnrasl/slw101

The low abundance and insignificance of dark discs in simulated Milky Way galaxies

2016· article· en· W2380262006 on OpenAlexaff
Matthieu Schaller, Carlos S. Frenk, Azadeh Fattahi, Julio F. Navarro, Kyle A. Oman, Till Sawala

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society Letters · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Victoria
FundersScience and Technology Facilities Council
KeywordsMilky WayPhysicsAstrophysicsDark matterDark matter haloAstronomyDwarf galaxy problemGalaxyCuspy halo problemLocal GroupHalo

Abstract

fetched live from OpenAlex

Abstract We investigate the presence and importance of dark matter discs in a sample of 24 simulated Milky Way galaxies in the apostle project, part of the eagle programme of hydrodynamic simulations in ΛCDM cosmology. It has been suggested that a dark disc in the Milky Way may boost the dark matter density and modify the velocity modulus relative to a smooth halo at the position of the Sun, with ramifications for direct detection experiments. From a kinematic decomposition of the dark matter and a real space analysis of all 24 haloes, we find that only one of the simulated Milky Way analogues has a detectable dark disc component. This unique event was caused by a merger at late time with an LMC-mass satellite at very low grazing angle. Considering that even this rare scenario only enhances the dark matter density at the solar radius by 35 per cent and affects the high-energy tail of the dark matter velocity distribution by less than 1 per cent, we conclude that the presence of a dark disc in the Milky Way is unlikely, and is very unlikely to have a significant effect on direct detection experiments.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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

Citations21
Published2016
Admission routes1
Has abstractyes

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