15 research outputs found
Bayesian semi-blind component separation for foreground removal in interferometric 21-cm observations
We present in this paper a new Bayesian semi-blind approach for foreground
removal in observations of the 21-cm signal with interferometers. The
technique, which we call HIEMICA (HI Expectation-Maximization Independent
Component Analysis), is an extension of the Independent Component Analysis
(ICA) technique developed for two-dimensional (2D) CMB maps to
three-dimensional (3D) 21-cm cosmological signals measured by interferometers.
This technique provides a fully Bayesian inference of power spectra and maps
and separates the foregrounds from signal based on the diversity of their power
spectra. Only relying on the statistical independence of the components, this
approach can jointly estimate the 3D power spectrum of the 21-cm signal and,
the 2D angular power spectrum and the frequency dependence of each foreground
component, without any prior assumptions about foregrounds. This approach has
been tested extensively by applying it to mock data from interferometric 21-cm
intensity mapping observations under idealized assumptions of instrumental
effects. We also discuss the impact when the noise properties are not known
completely. As a first step toward solving the 21 cm power spectrum analysis
problem we compare the semi-blind HIEMICA technique with the commonly used
Principal Component Analysis (PCA). Under the same idealized circumstances the
proposed technique provides significantly improved recovery of the power
spectrum. This technique can be applied straightforwardly to all 21-cm
interferometric observations, including epoch of reionization measurements, and
can be extended to single-dish observations as well.Comment: 18 pages, 7 figures, added some discussions about the impact of noise
misspecificatio
Systematic Effects in Interferometric Observations of the CMB Polarization
The detection of the primordial -mode spectrum of the polarized cosmic
microwave background (CMB) signal may provide a probe of inflation. However,
observation of such a faint signal requires excellent control of systematic
errors. Interferometry proves to be a promising approach for overcoming such a
challenge. In this paper we present a complete simulation pipeline of
interferometric observations of CMB polarization, including systematic errors.
We employ two different methods for obtaining the power spectra from mock data
produced by simulated observations: the maximum likelihood method and the
method of Gibbs sampling. We show that the results from both methods are
consistent with each other, as well as, within a factor of 6, with analytical
estimates. Several categories of systematic errors are considered: instrumental
errors, consisting of antenna gain and antenna coupling errors, and beam
errors, consisting of antenna pointing errors, beam cross-polarization and beam
shape (and size) errors. In order to recover the tensor-to-scalar ratio, ,
within a 10% tolerance level, which ensures the experiment is sensitive enough
to detect the -signal at in the multipole range ,
we find that, for a QUBIC-like experiment, Gaussian-distributed systematic
errors must be controlled with precisions of for antenna
gain, for antenna coupling, for pointing, for beam
shape, and for beam cross-polarization.Comment: 15 pages, 6 figures, submitted to ApJ
Maximum likelihood analysis of systematic errors in interferometric observations of the cosmic microwave background
We investigate the impact of instrumental systematic errors in
interferometric measurements of the cosmic microwave background (CMB)
temperature and polarization power spectra. We simulate interferometric CMB
observations to generate mock visibilities and estimate power spectra using the
statistically optimal maximum likelihood technique. We define a quadratic error
measure to determine allowable levels of systematic error that do not induce
power spectrum errors beyond a given tolerance. As an example, in this study we
focus on differential pointing errors. The effects of other systematics can be
simulated by this pipeline in a straightforward manner. We find that, in order
to accurately recover the underlying B-modes for r=0.01 at 28<l<384,
Gaussian-distributed pointing errors must be controlled to 0.7^\circ rms for an
interferometer with an antenna configuration similar to QUBIC, in agreement
with analytical estimates. Only the statistical uncertainty for 28<l<88 would
be changed at ~10% level. With the same instrumental configuration, we find the
pointing errors would slightly bias the 2-\sigma upper limit of the
tensor-to-scalar ratio r by ~10%. We also show that the impact of pointing
errors on the TB and EB measurements is negligibly small.Comment: 10 pages, 4 figures, accepted for publication in ApJS. Includes
improvements in clarity of presentation and Fig.4 added, in response to
refere
A 3D model of polarized dust emission in the Milky Way
International audienceWe present a three-dimensional model of polarized galactic dust emission that takes into account the variation of the dust density, spectral index and temperature along the line of sight, and contains randomly generated small-scale polarization fluctuations. The model is constrained to match observed dust emission on large scales, and match on smaller scales extrapolations of observed intensity and polarization power spectra. This model can be used to investigate the impact of plausible complexity of the polarized dust foreground emission on the analysis and interpretation of future cosmic microwave background polarization observations
Probabilistic image reconstruction for radio interferometers
International audienceWe present a novel, general-purpose method for deconvolving and denoizing images from gridded radio interferometric visibilities using Bayesian inference based on a Gaussian process model. The method automatically takes into account incomplete coverage of the uv-plane, signal mode coupling due to the primary beam and noise mode coupling due to uv sampling. Our method uses Gibbs sampling to efficiently explore the full posterior distribution of the underlying signal image given the data. We use a set of widely diverse mock images with a realistic interferometer set-up and level of noise to assess the method. Compared to results from a proxy for point source-based CLEAN method we find that in terms of rms error and signal-to-noise ratio our approach performs better than traditional deconvolution techniques, regardless of the structure of the source image in our test suite. Our implementation scales as O(n_p log n_p) provides full statistical and uncertainty information of the reconstructed image, requires no supervision and provides a robust, consistent framework for incorporating noise and parameter marginalizations and foreground removal