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Record W2996717096 · doi:10.1103/physrevd.101.123504

Minimizing gravitational lensing contributions to the primordial bispectrum covariance

2020· article· en· W2996717096 on OpenAlexaff
William R. Coulton, P. Daniel Meerburg, David G. Baker, Selim C. Hotinli, Adriaan J. Duivenvoorden, Alexander van Engelen

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsCanadian Institute for Theoretical Astrophysics
FundersScience and Technology Facilities CouncilOffice of ScienceNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversity of CambridgeNational Energy Research Scientific Computing CenterU.S. Department of Energy
KeywordsCosmic microwave backgroundPhysicsCosmic varianceBispectrumWeak gravitational lensingGravitational lensCovarianceAstrophysicsPlanckTrispectrumNon-GaussianityEstimatorPolarization (electrochemistry)CosmologyCosmic background radiationAnisotropyGalaxySpectral densityRedshiftOpticsStatisticsMathematics

Abstract

fetched live from OpenAlex

The next generation of ground-based cosmic microwave background (CMB) experiments aim to measure temperature and polarization fluctuations up to ${\ensuremath{\ell}}_{\mathrm{max}}\ensuremath{\approx}5000$ over half of the sky. Combined with Planck data on large scales, this will provide improved constraints on primordial non-Gaussianity. However, the impressive resolution of these experiments will come at a price. Besides signal confusion from galactic foregrounds, extragalactic foregrounds, and late-time gravitational effects, gravitational lensing will introduce large non-Gaussianity that can become the leading contribution to the bispectrum covariance through the connected four-point function. Here, we compute this effect analytically for the first time on the full sky for both temperature and polarization. We compare our analytical results with those obtained directly from map-based simulations of the CMB sky for several levels of instrumental noise. Of the standard shapes considered in the literature, the local shape is most affected, resulting in a 35% increase of the estimator standard deviation for an experiment such as the Simons Observatory (SO) and a 110% increase for a cosmic-variance limited experiment, including both temperature and polarization modes up to ${\ensuremath{\ell}}_{\mathrm{max}}=3800$. Because of the nature of the lensing four-point function, the impact on other shapes is reduced while still non-negligible for the orthogonal shape. Two possible avenues to reduce the non-Gaussian contribution to the covariance are proposed: First by marginalizing over lensing contributions, such as the Integrated Sachs Wolfe (ISW)-lensing three-point function in temperature, and second by delensing the CMB. We show the latter method can remove almost all extra covariance, reducing the effect to below $<5%$ for local bispectra. At the same time, delensing would remove signal biases from secondaries induced by lensing, such as ISW lensing. We aim to apply both techniques directly to the forthcoming SO data when searching for primordial non-Gaussianity.

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)0.001
metaresearch head score (Gemma)0.007
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.017
GPT teacher head0.410
Teacher spread0.393 · 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

Citations27
Published2020
Admission routes1
Has abstractyes

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