English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

An estimation of the sky signal from streams of Time Ordered Data (TOD)acquired by Cosmic Microwave Background (\cmb) experiments is one of the mostimportant steps in the context of \cmb data analysis referred to as themap-making problem. The continuously growing \cmb data sets render the \cmbmap-making problem more challenging in terms of computational cost and memoryin particular in the context of ground based experiments. In this context, westudy a novel class of the Preconditioned Conjugate Gradient (PCG) solverswhich invoke two-level preconditioners. We compare them against PCG solverscommonly used in the map-making context considering their precision andtime-to-solution. We compare these new methods on realistic, simulated datasets reflecting the characteristics of current and forthcoming \cmbground-based experiment. We develop an embarrassingly parallel implementationof the approach where each processor performs a sequential map-making for asubset of the TOD. We find that considering the map level residuals the newclass of solvers permits achieving tolerance of up to 3 orders of magnitudebetter than the standard approach, where the residual level often saturatesbefore convergence is reached. This corresponds to an important improvement inthe precision of recovered power spectra in particular on the largest angularscales. The new method also typically requires fewer iterations to reach arequired precision and thus shorter runtimes for a single map-making solution.However, the construction of an appropriate two-level preconditioner can be ascostly as a single standard map-making run. Nevertheless, if the same problemneeds to be solved multiple times, e.g., as in Monte Carlo simulations, thiscost has to be incurred only once, and the method should be competitive notonly as far as its precision but also its performance is concerned.

Iterative map-making with two-level preconditioning for polarized Cosmic Microwave Background data sets