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Record W4403942691 · doi:10.1088/1475-7516/2024/10/107

Axion-induced patchy screening of the Cosmic Microwave Background

2024· article· en· W4403942691 on OpenAlexaff
Cristina Mondino, Dalila Pîrvu, Junwu Huang, Matthew C. Johnson

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsYork UniversityUniversity of WaterlooPerimeter Institute
Fundersnot available
KeywordsPhysicsCosmic microwave backgroundAxionCosmic background radiationCOSMIC cancer databaseCosmological perturbation theoryAstronomyAstrophysicsDark matterCosmologyBackground radiationParticle physicsNuclear physicsRadiationQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Cosmic Microwave Background (CMB) photons can undergo resonant conversion into axions in the presence of magnetized plasma distributed inside non-linear large-scale structure (LSS). This process leads to axion-induced patchy screening: secondary temperature and polarization ani­sotropies with a characteristic non-blackbody frequency dependence that are strongly correlated with the distribution of LSS along our past light cone. We compute the axion-induced patchy screening contribution to two- and three- point correlation functions that include CMB anisotropies and tracers of LSS within the halo model. We use these results to forecast the sensitivity of existing and future surveys to photon-axion couplings for axion masses between 2 × 10 -13 eV and 3 × 10 -12 eV, using a combination of empirical estimates from Planck data of the contribution from instrumental noise and foregrounds as well as modeled contributions on angular scales only accessible with future datasets. We demonstrate that an analysis using Planck and the unWISE galaxy catalogue would be complementary to the most sensitive existing astrophysical axion searches, probing couplings as small as 3 × 10 -12 GeV -1 , while observations from a future survey such as CMB-S4 could extend this reach by almost an additional order of magnitude.

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 score0.764
Threshold uncertainty score0.302

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 head0.019
GPT teacher head0.254
Teacher spread0.235 · 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

Citations13
Published2024
Admission routes1
Has abstractyes

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