# Cosmic Microwave Background Mapmaking with a Messenger Field

## TL;DR

The paper introduces a messenger field method for faster, more accurate CMB mapmaking that outperforms traditional algorithms in convergence speed and large-scale recovery, suitable for large datasets.

## Contribution

It presents a novel messenger field approach that improves convergence speed and accuracy in CMB mapmaking without requiring preconditioners.

## Key findings

- Faster convergence than conjugate gradient methods.
- Better recovery of large-scale features.
- Lower overall chi-squared values.

## Abstract

We apply a messenger field method to solve the linear minimum-variance mapmaking equation in the context of Cosmic Microwave Background (CMB) observations. In simulations, the method produces sky maps that converge significantly faster than those from a conjugate gradient descent algorithm with a diagonal preconditioner, even though the computational cost per iteration is similar. The messenger method recovers large scales in the map better than conjugate gradient descent, and yields a lower overall $\chi^2$. In the single, pencil beam approximation, each iteration of the messenger mapmaking procedure produces an unbiased map, and the iterations become more optimal as they proceed. A variant of the method can handle differential data or perform deconvolution mapmaking. The messenger method requires no preconditioner, but a high-quality solution needs a cooling parameter to control the convergence. We study the convergence properties of this new method, and discuss how the algorithm is feasible for the large data sets of current and future CMB experiments.