scholarly journals Cooling Improves Cosmic Microwave Background Map-making when Low-frequency Noise is Large

2021 ◽  
Vol 922 (2) ◽  
pp. 97
Author(s):  
Bai-Chiang Chiang ◽  
Kevin M. Huffenberger

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 nonuniform 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.

2017 ◽  
Vol 600 ◽  
pp. A60 ◽  
Author(s):  
Davide Poletti ◽  
Giulio Fabbian ◽  
Maude Le Jeune ◽  
Julien Peloton ◽  
Kam Arnold ◽  
...  

Analysis of cosmic microwave background (CMB) datasets typically requires some filtering of the raw time-ordered data. For instance, in the context of ground-based observations, filtering is frequently used to minimize the impact of low frequency noise, atmospheric contributions and/or scan synchronous signals on the resulting maps. In this work we have explicitly constructed a general filtering operator, which can unambiguously remove any set of unwanted modes in the data, and then amend the map-making procedure in order to incorporate and correct for it. We show that such an approach is mathematically equivalent to the solution of a problem in which the sky signal and unwanted modes are estimated simultaneously and the latter are marginalized over. We investigated the conditions under which this amended map-making procedure can render an unbiased estimate of the sky signal in realistic circumstances. We then discuss the potential implications of these observations on the choice of map-making and power spectrum estimation approaches in the context of B-mode polarization studies. Specifically, we have studied the effects of time-domain filtering on the noise correlation structure in the map domain, as well as impact it may haveon the performance of the popular pseudo-spectrum estimators. We conclude that although maps produced by the proposed estimators arguably provide the most faithful representation of the sky possible given the data, they may not straightforwardly lead to the best constraints on the power spectra of the underlying sky signal and special care may need to be taken to ensure this is the case. By contrast, simplified map-makers which do not explicitly correct for time-domain filtering, but leave it to subsequent steps in the data analysis, may perform equally well and be easier and faster to implement. We focused on polarization-sensitive measurements targeting the B-mode component of the CMB signal and apply the proposed methods to realistic simulations based on characteristics of an actual CMB polarization experiment, POLARBEAR. Our analysis and conclusions are however more generally applicable.


Vestnik MEI ◽  
2018 ◽  
Vol 5 (5) ◽  
pp. 120-127
Author(s):  
Mikhail D. Vorobyev ◽  
◽  
Dmitriy N. Yudaev ◽  
Andrey Yu. Zorin ◽  
◽  
...  

1999 ◽  
Author(s):  
Charles K. Birdsall ◽  
J. P. Varboncoeur ◽  
P. J. Christensen

2021 ◽  
Vol 182 ◽  
pp. 108203
Author(s):  
Lígia T. Silva ◽  
Alda Magalhães ◽  
José Ferreira Silva ◽  
Fernando Fonseca

Measurement ◽  
2021 ◽  
pp. 109867
Author(s):  
Krzysztof ACHTENBERG ◽  
Janusz MIKOŁAJCZYK ◽  
Carmine CIOFI ◽  
Graziella SCANDURRA ◽  
Krystian MICHALCZEWSKI ◽  
...  

2021 ◽  
pp. 108050
Author(s):  
Maria Glória Caño de Andrade ◽  
Luis Felipe de Oliveira Bergamim ◽  
Braz Baptista Júnior ◽  
Carlos Roberto Nogueira ◽  
Fábio Alex da Silva ◽  
...  

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