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Record W4414968896 · doi:10.1088/1475-7516/2025/10/040

Early growth of structure in warm wave dark matter

2025· article· en· W4414968896 on OpenAlexaff
Mustafa A. Amin, Simon May, Mehrdad Mirbabayi

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsPerimeter Institute
Fundersnot available
KeywordsDark matterAdiabatic processHaloMatter power spectrumSpectral densityStructure formationDark matter haloCold dark matterHot dark matter

Abstract

fetched live from OpenAlex

Abstract We explore the growth of structure in wave-like dark matter models, where the field and density spectra are peaked at sub-horizon wavenumbers. Starting with the Schrödinger-Poisson system, we derive the scale-dependent evolution of the matter power spectrum during radiation and matter domination. We find a suppression of adiabatic perturbations during radiation domination, controlled by a free-streaming length, and scale-dependent growth of the initially white-noise isocurvature power, controlled by a Jeans scale during matter domination. The results are in qualitative, and in some regimes quantitative, agreement with the quasi-particle picture. We verify the analytic results of the power spectrum with 3+1-dimensional cosmological Schrödinger-Poisson simulations. We propose an analytic formula for the halo mass function, which is in rough agreement with the simulation results at early times after matter-radiation equality. Our simulations show that early halos typically host a soliton.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

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 communication0.0010.001
Open science0.0000.001
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 head0.005
GPT teacher head0.220
Teacher spread0.214 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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