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Record W1971308472 · doi:10.1103/physrevd.90.043524

Scattering, damping, and acoustic oscillations: Simulating the structure of dark matter halos with relativistic force carriers

2014· article· en· W1971308472 on OpenAlexafffund
Matthew R. Buckley, Jesús Zavala, Francis-Yan Cyr-Racine, Kris Sigurdson, Mark Vogelsberger

Bibliographic record

VenuePhysical review. D. Particles, fields, gravitation, and cosmology/Physical review. D, Particles, fields, gravitation, and cosmology · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsPhysicsDark matterHaloPhenomenology (philosophy)Matter power spectrumAstrophysicsScalar field dark matterCold dark matterHot dark matterDark fluidDark matter haloSpectral densityDark energyCosmologyGalaxy

Abstract

fetched live from OpenAlex

We demonstrate that self-interacting dark matter models with interactions mediated by light particles can have significant deviations in the matter power spectrum and detailed structure of galactic halos when compared to a standard cold dark matter scenario. While these deviations can take the form of suppression of small-scale structure that are in some ways similar to that of warm dark matter, the self-interacting models have a much wider range of possible phenomenology. A long-range force in the dark matter can introduce multiple scales to the initial power spectrum, in the form of dark acoustic oscillations and an exponential cutoff in the power spectrum. Using simulations we show that the impact of these scales can remain observationally relevant up to the present day. Furthermore, the self-interaction can continue to modify the small-scale structure of the dark matter halos, reducing their central densities and creating a dark matter core. The resulting phenomenology is unique to these type of models.

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 categoriesMeta-epidemiology (narrow)
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 score0.589
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.295
Teacher spread0.283 · 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.

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

Citations117
Published2014
Admission routes2
Has abstractyes

Explore more

Same venuePhysical review. D. Particles, fields, gravitation, and cosmology/Physical review. D, Particles, fields, gravitation, and cosmologySame topicDark Matter and Cosmic PhenomenaFrench-language works237,207