Lens covariance effects on likelihood analyses of CMB power spectra

TL;DR

This paper introduces an accurate likelihood model accounting for lensing-induced non-Gaussian correlations in CMB power spectra, crucial for precise parameter estimation in future experiments like CMB-S4.

Contribution

It presents a simple yet accurate likelihood model that includes lensing covariance effects, enabling better parameter estimation and consistency tests in CMB analyses.

Findings

Ignoring lensing covariance underestimates errors by over a factor of two.

Proper modeling prevents substantial misestimation of parameters like the dark energy equation of state.

The method allows separation of lensing and unlensed information for consistency checks.

Abstract

Non-Gaussian correlations induced in CMB power spectra by gravitational lensing must be included in likelihood analyses for future CMB experiments. We present a simple but accurate likelihood model which includes these correlations and use it for Markov Chain Monte Carlo parameter estimation from simulated lensed CMB maps in the context of $\Lambda$CDM and extensions which include the sum of neutrino masses or the dark energy equation of state $w$. If lensing-induced covariance is not taken into account for a CMB-S4 type experiment, the errors for one combination of parameters in each case would be underestimated by more then a factor of two and lower limits on $w$ could be misestimated substantially. The frequency of falsely ruling out the true model or finding tension with other data sets would also substantially increase. Our analysis also enables a separation of lens and unlensed information from CMB power spectra, which provides for consistency tests of the model and, if combined with other such measurements, a nearly lens-sample-variance free test for systematics and new physics in the unlensed spectrum. This parameterization also leads to a simple effective likelihood that can be used to assist model building in case consistency tests of $\Lambda$CDM fail.