CMB-S4 Science Book, First Edition

This book lays out the scientific goals to be addressed by the next-generation ground-based cosmic microwave background experiment, CMB-S4, envisioned to consist of dedicated telescopes at the South Pole, the high Chilean Atacama plateau and possibly a northern hemisphere site, all equipped with new superconducting cameras. CMB-S4 will dramatically advance cosmological studies by crossing critical thresholds in the search for the B-mode polarization signature of primordial gravitational waves, in the determination of the number and masses of the neutrinos, in the search for evidence of new light relics, in constraining the nature of dark energy, and in testing general relativity on large scales

Making maps of cosmic microwave background polarization for B-mode studies: The POLARBEAR example

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. \ua9 ESO, 2017

CMB-S4 Science Book, First Edition

This book lays out the scientific goals to be addressed by the next-generation ground-based cosmic microwave background experiment, CMB-S4, envisioned to consist of dedicated telescopes at the South Pole, the high Chilean Atacama plateau and possibly a northern hemisphere site, all equipped with new superconducting cameras. CMB-S4 will dramatically advance cosmological studies by crossing critical thresholds in the search for the B-mode polarization signature of primordial gravitational waves, in the determination of the number and masses of the neutrinos, in the search for evidence of new light relics, in constraining the nature of dark energy, and in testing general relativity on large scales

Adaptive Kernel Density Estimation proposal in gravitational wave data analysis

Markov Chain Monte Carlo approach is frequently used within Bayesian framework to sample the target posterior distribution. Its efficiency strongly depends on the proposal used to build the chain. The best jump proposal is the one that closely resembles the unknown target distribution, therefore we suggest an adaptive proposal based on Kernel Density Estimation (KDE). We group parameters of the model according to their correlation and build KDE based on the already accepted points for each group. We adapt the KDE-based proposal until it stabilizes. We argue that such a proposal could be helpful in applications where the data volume is increasing and in the hyper-model sampling. We tested it on several astrophysical datasets (IPTA and LISA) and have shown that in some cases KDE-based proposal also helps to reduce the autocorrelation length of the chains. The efficiency of this proposal is reduces in case of the strong correlations between a large group of parameters

Adaptive Kernel Density Estimation proposal in gravitational wave data analysis

Markov Chain Monte Carlo approach is frequently used within Bayesian framework to sample the target posterior distribution. Its efficiency strongly depends on the proposal used to build the chain. The best jump proposal is the one that closely resembles the unknown target distribution, therefore we suggest an adaptive proposal based on Kernel Density Estimation (KDE). We group parameters of the model according to their correlation and build KDE based on the already accepted points for each group. We adapt the KDE-based proposal until it stabilizes. We argue that such a proposal could be helpful in applications where the data volume is increasing and in the hyper-model sampling. We tested it on several astrophysical datasets (IPTA and LISA) and have shown that in some cases KDE-based proposal also helps to reduce the autocorrelation length of the chains. The efficiency of this proposal is reduces in case of the strong correlations between a large group of parameters

Adaptive Kernel Density Estimation proposal in gravitational wave data analysis

Markov Chain Monte Carlo approach is frequently used within Bayesian framework to sample the target posterior distribution. Its efficiency strongly depends on the proposal used to build the chain. The best jump proposal is the one that closely resembles the unknown target distribution, therefore we suggest an adaptive proposal based on Kernel Density Estimation (KDE). We group parameters of the model according to their correlation and build KDE based on the already accepted points for each group. We adapt the KDE-based proposal until it stabilizes. We argue that such a proposal could be helpful in applications where the data volume is increasing and in the hyper-model sampling. We tested it on several astrophysical datasets (IPTA and LISA) and have shown that in some cases KDE-based proposal also helps to reduce the autocorrelation length of the chains. The efficiency of this proposal is reduces in case of the strong correlations between a large group of parameters

Prototyping of the Distributed Data Processing Center of LISA

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