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Secondary infall and the pseudo-phase-space density profiles of cold dark matter haloes

2010· article· en· W1917574636 on OpenAlexaff

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsUniversity of Victoria
FundersScience and Technology Facilities Council
KeywordsCold dark matterRADIUSVirial theoremDark matterEllipsoidPower lawMeasure (data warehouse)Shell (structure)Shock (circulatory)

Abstract

fetched live from OpenAlex

We use N-body simulations to investigate the radial dependence of the density, ρ, and velocity dispersion, σ, in cold dark matter (CDM) haloes. In particular, we explore how closely Q≡ρ/σ3, a surrogate measure of the phase-space density, follows a power law in radius. Our study extends earlier work by considering, in addition to spherically averaged profiles, local Q estimates for individual particles, Qi; profiles based on the ellipsoidal radius dictated by the triaxial structure of the halo, Qi(r′); and by carefully removing substructures in order to focus on the profile of the smooth halo, Qs. The resulting Qsi(r′) profiles follow closely a power law near the centre, but show a clear upturn from this trend near the virial radius, r200. The location and magnitude of the deviations are in excellent agreement with the predictions from Bertschinger’s spherical secondary-infall similarity solution. In this model, Q∝r−1.875 in the inner, virialized regions, but departures from a power-law occur near r200 because of the proximity of this radius to the location of the first shell crossing – the shock radius in the case of a collisional fluid. Particles there have not yet fully virialized, and so Q departs from the inner power-law profile. Our results imply that the power-law nature of Q profiles only applies to the inner regions and cannot be used to predict accurately the structure of CDM haloes beyond their characteristic scale radius.

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 score0.067
Threshold uncertainty score0.449

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.004
GPT teacher head0.196
Teacher spread0.192 · 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

Citations61
Published2010
Admission routes1
Has abstractyes

Explore more

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