Improved estimation of cluster mass profiles from the cosmic microwave background

We develop a new method for reconstructing cluster-mass profiles and large-scale structure from the cosmic microwave background (CMB). By analyzing the likelihood of CMB lensing, we analytically prove that standard quadratic estimators for CMB lensing are unbiased and achieve the optimal condition only in the limit of no lensing; they become progressively biased and suboptimal, when the lensing effect is large, especially for clusters that can be found by ongoing Sunyaev-Zel'dovich surveys. Adopting an alternative approach to the CMB likelihood, we construct a new maximum likelihood estimator that utilizes delensed CMB temperature fields based on an assumed model. We analytically show that this estimator asymptotically approaches the optimal condition as our assumed model is refined, and we numerically show that as we iteratively apply it to CMB maps our estimator quickly converges to the true model with a factor of 10 less number of clusters than standard quadratic estimators need. For realistic CMB experiments, we demonstrate the applicability of the maximum likelihood estimator with tests against numerical simulations in the presence of CMB secondary contaminants. With significant improvement on the signal-to-noise ratio, our new maximum likelihood estimator can be used to measure the cluster-mass cross-correlation functions at different redshifts, probing the evolution of dark energy.