MétaCan
Menu
← Back to cohort
Record W2604769819 · doi:10.1103/physrevd.96.083519

Reconstructing the primary CMB dipole

2017· article· en· W2604769819 on OpenAlexaff
P. Daniel Meerburg, Joel Meyers, Alexander van Engelen

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
Fundersnot available
KeywordsCosmic microwave backgroundPrimary (astronomy)DipolePhysicsAstrophysicsAstronomyOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

The observed dipole anisotropy of the cosmic microwave background (CMB) temperature is much larger than the fluctuations observed on smaller scales, and it is dominated by the kinematic contribution from the Doppler boosting of the monopole due to our motion with respect to the CMB rest frame. In addition to this kinematic component, there is expected to be an intrinsic contribution with an amplitude about two orders of magnitude smaller. Here we explore a method whereby the intrinsic CMB dipole can be reconstructed through observation of temperature fluctuations on small scales that result from gravitational lensing. Though the experimental requirements pose practical challenges, we show that one can, in principle, achieve a cosmic variance limited measurement of the primary dipole using the reconstruction method we describe. Since the primary CMB dipole is sensitive to the largest observable scales, such a measurement would have a number of interesting applications for early universe physics, including testing large-scale anomalies, extending the lever-arm for measuring local non-Gaussianity, and constraining isocurvature fluctuations on superhorizon scales.

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)0.002
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.016
GPT teacher head0.415
Teacher spread0.398 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

Explore more

Same venuePhysical review. D/Physical review. D.→Same topicCosmology and Gravitation Theories→French-language works237,207→