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Record W4409918972 · doi:10.1088/1475-7516/2025/04/081

The effects of dark matter annihilation and dark matter-baryon velocity offsets at Cosmic Dawn

2025· article· en· W4409918972 on OpenAlexaff
Liqiang Hou, Katherine J. Mack

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsYork UniversityPerimeter InstituteUniversity of Waterloo
Fundersnot available
KeywordsPhysicsDark matterAnnihilationCOSMIC cancer databaseDark energyBaryonAstrophysicsScalar field dark matterMixed dark matterDark fluidBaryonic dark matterHot dark matterAstronomyCosmologyParticle physics

Abstract

fetched live from OpenAlex

Abstract Dark matter annihilation has the potential to leave an imprint on the properties of the first luminous structures at Cosmic Dawn as well as the overall evolution of the intergalactic medium (IGM). In this work, we employ a semi-analytic method to model dark matter annihilation during Cosmic Dawn (approximately redshift z = 20 to 40), examining potential modifications to IGM evolution as well as gas collapse, cooling, and star formation in mini-halos. Our analysis takes into account the effects of dark matter-baryon velocity offsets, utilizing the public 21cmvFAST code, and producing predictions for the 21cm global signal. The results from our simplified model suggest that dark matter annihilation can suppress the gas fraction in small halos and alter the molecular cooling process, while the impact on star formation might be positive or negative depending on parameters of the dark matter model as well as the redshift and assumptions about velocity offsets. This underscores the need for more comprehensive simulations of the effects of exotic energy injection at Cosmic Dawn as observational probes are providing us new insights into the process of reionization and the formation of first stars and galaxies.

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 score0.395
Threshold uncertainty score0.390

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 head0.004
GPT teacher head0.223
Teacher spread0.220 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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