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Record W4313994676 · doi:10.1088/1475-7516/2023/01/004

The effects of relativistic hidden sector particles on the matter power spectrum

2023· article· en· W4313994676 on OpenAlexaff
Himanish Ganjoo, Adrienne L. Erickcek, Weikang Lin, Katherine J. Mack

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsPerimeter InstituteUniversity of Waterloo
Fundersnot available
KeywordsHidden sectorPhysicsDark matterSpectral densityMatter power spectrumCold dark matterUniverseScale (ratio)AstrophysicsStatisticsCosmologyQuantum mechanicsDark energyMathematics

Abstract

fetched live from OpenAlex

Abstract If dark matter resides in a hidden sector minimally coupled to the Standard Model, another particle within the hidden sector might dominate the energy density of the early universe temporarily, causing an early matter-dominated era (EMDE). During an EMDE, matter perturbations grow more rapidly than they would in a period of radiation domination, which leads to the formation of microhalos much earlier than they would form in standard cosmological scenarios. These microhalos boost the dark matter annihilation signal, but this boost is highly sensitive to the small-scale cut-off in the matter power spectrum. If the dark matter is sufficiently cold, this cut-off is set by the relativistic pressure of the particle that dominates the hidden sector. We determine the evolution of dark matter density perturbations in this scenario, obtaining the power spectrum at the end of the EMDE. We analyze the suppression of perturbations due to the relativistic pressure of the dominant hidden sector particle and express the cut-off scale and peak scale for which the matter power spectrum is maximized in terms of the properties of this particle. We also supply transfer functions to relate the matter power spectrum with a small-scale cut-off resulting from the pressure of the dominant hidden sector particle to the matter power spectrum that results from a cold hidden sector. These transfer functions facilitate the quick computation of accurate matter power spectra in EMDE scenarios with initially hot hidden sectors and allow us to identify which models significantly enhance the microhalo abundance.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.208

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 science0.0000.000
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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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 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

Citations12
Published2023
Admission routes1
Has abstractyes

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