Cluster profiles from beyond-the-QE CMB lensing mass maps

TL;DR

This paper evaluates and enhances a maximum a posteriori (MAP) CMB lensing reconstruction method for galaxy clusters, demonstrating its effectiveness in reducing noise and bias, thereby improving cluster mass calibration.

Contribution

It extends the MAP CMB lensing reconstruction method to cluster scales, showing it reduces noise and bias without scale cuts, improving cluster mass calibration.

Findings

Delensing reduces noise variance around clusters.

Bias in temperature quadratic estimator is nearly eliminated.

MAP estimator is effective for cluster mass calibration.

Abstract

Clusters of galaxies, being the largest collapsed structures in the universe, offer valuable insights into the nature of cosmic evolution. Precise calibration of the mass of clusters can be obtained by extracting their gravitational lensing signal on the Cosmic Microwave Background (CMB) fluctuations. We extend and test here the performance achieved on cluster scales by the parameter-free, maximum a posteriori (MAP) CMB lensing reconstruction method, which has been shown to be optimal in the broader context of CMB lensing mass map and power spectrum estimation. In the context of cluster lensing, the lensing signal of other large-scale structures acts as an additional source of noise. We show here that by delensing the CMB fluctuations around each and every cluster, this noise variance is reduced according to expectations. We also demonstrate that the well-known bias in the temperature quadratic estimator in this regime, sourced by the strong non-Gaussianity of the signal, is almost entirely mitigated without any scale cuts. Being statistically speaking an optimal and blind lensing mass map reconstruction, the MAP estimator is a promising tool for the calibration of the masses of clusters.