Inference of gravitational lensing and patchy reionization with future CMB data

We develop an optimal Bayesian solution for jointly inferring secondary signals in the cosmic microwave background (CMB) originating from gravitational lensing and from patchy screening during the epoch of reionization. This method is able to extract full information content from the data, improving upon previously considered quadratic estimators for lensing and screening. We forecast constraints using the marginal unbiased score expansion method, and show that they are largely dominated by CMB polarization, and depend on the exact details of reionization. For models consistent with current data which produce the largest screening signals, a detection (3 sigma) of the cross-correlation between lensing and screening is possible with SPT-3G and a detection of the autocorrelation is possible with CMB-S4. Models with the lowest screening signals evade the sensitivity of SPT-3G but are still possible to detect with CMB-S4 via their lensing cross-correlation.

Automated summary

We have developed an optimal Bayesian solution to jointly infer secondary signals in the cosmic microwave background (CMB) from gravitational lensing and patchy screening during reionization. This method extracts the complete information content from the data and improves upon previous quadratic estimators. Our forecast constraints, obtained using the marginal unbiased score expansion method, show that the signal is mainly dominated by CMB polarization and depends on the specific details of reionization. For models consistent with current data, SPT-3G can detect the cross-correlation between lensing and screening, while CMB-S4 can detect the autocorrelation. Models with lower screening signals can still be detected with CMB-S4 through their lensing cross-correlation.