text: 15,114,1225157385397985280,4189511729,Marius Millea,"Detecting the CMB polarization B-modes from primordial gravitational waves would revolutionize our understanding of inflation. But this signal is highly obscured by gravitational lensing distortion. Our new paper shows how this can be cleaned optimally. <LINK> <LINK> The method is fully Bayesian and so can take the data and reconstruct all of the available information about the projected mass causing the gravitational lensing, the primordial signal, and parameters controlling the statistics of these fields. In practice our code samples the ~million dimensional parameter space, which we can do efficiently thanks to #JuliaLang, GPUs, Hamiltonian Monte Carlo, and some important tricks we discuss in the paper. The software package is ready for anyone to run: <LINK> Excited to have this work with @EthanAnderes and @bwandelt out so we can build towards using this for current and future CMB experiments.",https://arxiv.org/abs/2002.00965,"The search for primordial gravitational waves in the Cosmic Microwave Background (CMB) will soon be limited by our ability to remove the lensing contamination to $B$-mode polarization. The often-used quadratic estimator for lensing is known to be suboptimal for surveys that are currently operating and will continue to become less and less efficient as instrumental noise decreases. While foregrounds can in principle be mitigated by observing in more frequency bands, progress in delensing hinges entirely on algorithmic advances. We demonstrate here a new inference method that solves this problem by sampling the exact Bayesian posterior of any desired cosmological parameters, of the gravitational lensing potential, and of the delensed CMB maps, given lensed temperature and polarization data. We validate the method using simulated CMB data with non-white noise and masking on up to 650\,deg$^2$ patches of sky. A unique strength of this approach is the ability to jointly estimate cosmological parameters which control both the primordial CMB and the lensing potential, which we demonstrate here for the first time by sampling both the tensor-to-scalar ratio, $r$, and the amplitude of the lensing potential, $A_\phi$. The method allows us to perform the most precise check to-date of several important approximations underlying CMB-S4 $r$ forecasting, and we confirm these yield the correct expected uncertainty on $r$ to better than 10%. ","Bayesian delensing delight: sampling-based inference of the primordial