Cooling Improves Cosmic Microwave Background Map-Making When Low-Frequency Noise is Large

TL;DR

This paper introduces a cooling method for iterative map-making in CMB data analysis, which improves convergence and accuracy in the presence of significant low-frequency noise by adaptively adjusting the noise covariance during the solution process.

Contribution

The paper presents a novel cooling approach that enhances the efficiency and fidelity of conjugate gradient solutions in CMB map-making with high low-frequency noise.

Findings

Cooling accelerates convergence in noisy conditions

Higher fidelity maps are produced with the cooling method

Analytic estimates guide parameter selection for the approach

Abstract

In the context of Cosmic Microwave Background data analysis, we study the solution to the equation that transforms scanning data into a map. As originally suggested in "messenger" methods for solving linear systems, we split the noise covariance into uniform and non-uniform parts and adjust their relative weights during the iterative solution. With simulations, we study mock instrumental data with different noise properties, and find that this "cooling" or perturbative approach is particularly effective when there is significant low-frequency noise in the timestream. In such cases, a conjugate gradient algorithm applied to this modified system converges faster and to a higher fidelity solution than the standard conjugate gradient approach. We give an analytic estimate for the parameter that controls how gradually the linear system should change during the course of the solution.