相关论文: Fast Wiener filtering of CMB maps

In order to draw scientific conclusions from observations of cosmic microwave background (CMB) polarization, it is necessary to separate the contributions of the E and B components of the data. For data with incomplete sky coverage, there…

We describe an algorithm for the extraction of the angular power spectrum of an intensity field, such as the cosmic microwave background (CMB), from interferometer data. This new method, based on the gridding of interferometer visibilities…

We derive an optimal linear filter to suppress the noise from the COBE DMR sky maps for a given power spectrum. We then apply the filter to the first-year DMR data, after removing pixels within $20^\circ$ of the Galactic plane from the…

One goal of CMB data analysis is to combine data at different frequencies, angular resolutions, and noise levels in order to best extract the component with a Plankian spectral behaviour. A multi-frequency Wiener filtering method has been…

A new generation of wide-field radio interferometers designed for 21-cm surveys is being built as drift scan instruments allowing them to observe large fractions of the sky. With large numbers of antennas and frequency channels the enormous…

In this paper, we consider Wiener filters to reconstruct deterministic and (wide-band) stationary graph signals from their observations corrupted by random noises, and we propose distributed algorithms to implement Wiener filters and…

We estimate the accuracy with which various cosmological parameters can be determined from the CMB temperature and polarization data when various galactic unpolarized and polarized foregrounds are included and marginalized using the…

Analysis of cosmic microwave background (CMB) datasets typically requires some filtering of the raw time-ordered data. Filtering is frequently used to minimize the impact of low frequency noise, atmospheric contributions and/or scan…

High fidelity separation of astrophysical foreground contributions from the cosmic microwave background (CMB) signal has been recognized as one of the main challenges of modern CMB data analysis, and one which needs to be addressed in a…

We propose a power filter Gp for linear reconstruction of the CMB signal from observational maps. This Gp filter preserves the power spectrum of the CMB signal in contrast to the Wiener filter which diminishes the power spectrum of the…

As Cosmic Microwave Background (CMB) measurements are becoming more ambitious, the issue of foreground contamination is becoming more pressing. This is especially true at the level of sensitivity, angular resolution and for the sky coverage…

A new method for removing point radio sources and other non-Gaussian noise is proposed as a means of improving the accuracy of estimates of the angular power spectrum of the cosmic microwave background (CMB). The main idea of the method is…

We describe a new map-making code for cosmic microwave background (CMB) observations. It implements fast algorithms for convolution and transpose convolution of two functions on the sphere (Wandelt & G\'{o}rski 2001). Our code can account…

We consider the problem of robust deconvolution, and particularly the recovery of an unknown deterministic signal convolved with a known filter and corrupted by additive noise. We present a novel, non-iterative data-driven approach.…

One major challenge for living cells is the measurement and prediction of signals corrupted by noise. In general, cells need to make decisions based on their compressed representation of noisy, time-varying signals. Strategies for signal…

We present a method of subtracting the foreground contamination for the measurement of CMB polarization. We calculate the resultant errors on CMB polarization and temperature-polarization cross correlation power spectra for the high…

Quantitative evaluations of differences and/or similarities between data samples define and shape optimisation problems associated with learning data distributions. Current methods to compare data often suffer from limitations in capturing…

This paper is concerned with the channel estimation problem in millimetre wave (MMW) wireless systems with large antenna arrays. By exploiting the sparse nature of the MMW channel, we present an efficient estimation algorithm based on a…

We present a new Unbiased Minimal Variance (UMV) estimator for the purpose of reconstructing the large--scale structure of the universe from noisy, sparse and incomplete data. Similar to the Wiener Filter (WF), the UMV estimator is derived…

Massive MIMO is a variant of multiuser MIMO in which the number of antennas at the base station (BS) $M$ is very large and typically much larger than the number of served users (data streams) $K$. Recent research has illustrated the…