Cosmic Microwave Background Mapmaking with a Messenger Field

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.

12 pages, 6 figures, revision includes new sections that treat differential data. Submitted to ApJ

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Cited by in corpus (6)

• Wiener filtering and pure E/B decomposition of CMB maps with anisotropic correlated noise
• Optimal and fast E/B separation with a dual messenger field
• Iterative map-making with two-level preconditioning for polarized Cosmic Microwave Background data sets
• Solving linear equations with messenger-field and conjugate gradients techniques - an application to CMB data analysis
• MAPPRAISER: A massively parallel map-making framework for multi-kilo pixel CMB experiments
• Cooling Improves Cosmic Microwave Background Map-Making When Low-Frequency Noise is Large