Optimal CMB Lensing Reconstruction and Parameter Estimation with SPTpol Data

We perform the first simultaneous Bayesian parameter inference and optimal reconstruction of the gravitational lensing of the cosmic microwave background (CMB), using 100 deg$^2$ of polarization observations from the SPTpol receiver on the South Pole Telescope. These data reach noise levels as low as 5.8 $\mu$K-arcmin in polarization, which are low enough that the typically used quadratic estimator (QE) technique for analyzing CMB lensing is significantly sub-optimal. Conversely, the Bayesian procedure extracts all lensing information from the data and is optimal at any noise level. We infer the amplitude of the gravitational lensing potential to be $A_\phi\,{=}\,0.949\,{\pm}\,0.122$ using the Bayesian pipeline, consistent with our QE pipeline result, but with 17\% smaller error bars. The Bayesian analysis also provides a simple way to account for systematic uncertainties, performing a similar job as frequentist "bias hardening," and reducing the systematic uncertainty on $A_\phi$ due to polarization calibration from almost half of the statistical error to effectively zero. Finally, we jointly constrain $A_\phi$ along with $A_{\rm L}$, the amplitude of lensing-like effects on the CMB power spectra, demonstrating that the Bayesian method can be used to easily infer parameters both from an optimal lensing reconstruction and from the delensed CMB, while exactly accounting for the correlation between the two. These results demonstrate the feasibility of the Bayesian approach on real data, and pave the way for future analysis of deep CMB polarization measurements with SPT-3G, Simons Observatory, and CMB-S4, where improvements relative to the QE can reach 1.5 times tighter constraints on $A_\phi$ and 7 times lower effective lensing reconstruction noise.