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The significance of the integrated Sachs-Wolfe effect revisited

2012· article· en· W2108465388 on OpenAlexfundno aff
T. Giannantonio, Robert Crittenden, R. C. Nichol, Ashley J. Ross

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryYork UniversityScience and Technology Facilities CouncilCarnegie Mellon UniversityOffice of ScienceJohns Hopkins UniversityCollege of Engineering, Michigan State UniversityHarvard UniversityOhio State UniversityNew Mexico State UniversityUniversity of PortsmouthYale UniversityVanderbilt UniversityUniversity of ArizonaAlexander von Humboldt-StiftungNational Science FoundationUniversity of WashingtonAlfred P. Sloan FoundationPrinceton UniversityBrookhaven National LaboratoryU.S. Department of Energy
KeywordsPhysicsCosmic microwave backgroundCovariance matrixSkyCovarianceSigmaData setRotation (mathematics)COSMIC cancer databaseCosmic background radiationAstrophysicsCosmic varianceSet (abstract data type)AlgorithmAstronomyStatisticsOpticsArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

We revisit the state of the integrated Sachs–Wolfe (ISW) effect measurements in light of newly available data and address criticisms about the measurements which have recently been raised. We update the data set previously assembled by Giannantonio et al. to include new data releases for both the cosmic microwave background and the large-scale structure of the Universe. We find that our updated results are consistent with previous measurements. By fitting a single template amplitude, we now obtain a combined significance of the ISW detection at the 4.4σ level, which fluctuates by ∼0.4σ when alternative data cuts and analysis assumptions are considered. We also make new tests for systematic contaminations of the data, focusing in particular on the issues raised by Sawangwit et al. Amongst them, we address the rotation test, which aims at checking for possible systematics by correlating pairs of randomly rotated maps. We find results consistent with the expected data covariance, no evidence for enhanced correlation on any preferred axis of rotation, and therefore no indication of any additional systematic contamination. We publicly release the results, the covariance matrix and the sky maps used to obtain them.

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)0.001
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 score0.015
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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

Citations104
Published2012
Admission routes1
Has abstractyes

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