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Record W2267567204 · doi:10.1103/physrevd.95.063011

Testing physical models for dipolar asymmetry: From temperature to<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>space to lensing

2017· article· lv· W2267567204 on OpenAlexafffund
J. P. Zibin, D. Contreras

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2017
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsCosmic microwave backgroundPlanckAsymmetryPhysicsCosmic background radiationDipolePolarization (electrochemistry)Scalar (mathematics)Statistical physicsAstrophysicsAnisotropyParticle physicsOpticsQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

One of the most intriguing hints of a departure from the standard cosmological model is a large-scale dipolar power asymmetry in the cosmic microwave background (CMB). If not a statistical fluke, its origins must lie in the modulation of the position-space fluctuations via a physical mechanism, which requires the observation of new modes to confirm or refute. We introduce an approach to describe such a modulation in $k$ space and calculate its effects on the CMB temperature and lensing. We fit the $k$-space modulation parameters to Planck 2015 temperature data and show that CMB lensing will not provide us with enough independent information to confirm or refute such a mechanism. However, our approach elucidates some poorly understood aspects of the asymmetry, in particular that it is weakly constrained. Also, it will be particularly useful in predicting the effectiveness of polarization in testing a physical modulation.

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)0.004
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.338
Teacher spread0.316 · 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

Citations17
Published2017
Admission routes2
Has abstractyes

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