233 research outputs found
Adaptive Weighting in Radio Interferometric Imaging
Radio interferometers observe the Fourier space of the sky, at locations
determined by the array geometry. Before a real space image is constructed by a
Fourier transform, the data is weighted to improve the quality of
reconstruction. Two criteria for calculation of weights are maximizing
sensitivity and minimizing point spread function (PSF) sidelobe levels. In this
paper, we propose a novel weighting scheme suitable for ultra deep imaging
experiments. The proposed weighting scheme is used to maximize sensitivity
while minimizing PSF sidelobe variation across frequency and multiple epochs.
We give simulation results that show the superiority of the proposed scheme
compared with commonly used weighting schemes in achieving these objectives.Comment: MNRAS Accepted 2014 July 22. Received 2014 July 15; in original form
2014 June 2
Radio Interferometric Calibration Using a Riemannian Manifold
In order to cope with the increased data volumes generated by modern radio
interferometers such as LOFAR (Low Frequency Array) or SKA (Square Kilometre
Array), fast and efficient calibration algorithms are essential. Traditional
radio interferometric calibration is performed using nonlinear optimization
techniques such as the Levenberg-Marquardt algorithm in Euclidean space. In
this paper, we reformulate radio interferometric calibration as a nonlinear
optimization problem on a Riemannian manifold. The reformulated calibration
problem is solved using the Riemannian trust-region method. We show that
calibration on a Riemannian manifold has faster convergence with reduced
computational cost compared to conventional calibration in Euclidean space.Comment: Draft version. Final version will appear in IEEE ICASSP 2013,
http://www.icassp2013.com
Fundamental Limitations of Pixel Based Image Deconvolution in Radio Astronomy
Deconvolution is essential for radio interferometric imaging to produce
scientific quality data because of finite sampling in the Fourier plane. Most
deconvolution algorithms are based on CLEAN which uses a grid of image pixels,
or clean components. A critical matter in this process is the selection of
pixel size for optimal results in deconvolution. As a rule of thumb, the pixel
size is chosen smaller than the resolution dictated by the interferometer. For
images consisting of unresolved (or point like) sources, this approach yields
optimal results. However, for sources that are not point like, in particular
for partially resolved sources, the selection of right pixel size is still an
open issue. In this paper, we investigate the limitations of pixelization in
deconvolving extended sources. In particular, we pursue the usage of
orthonormal basis functions to model extended sources yielding better results
than by using clean components.Comment: 4 pages, 5 figures, the 6th IEEE Sensor Array and Multichannel Signal
Processing worksho
Robust Radio Interferometric Calibration Using the t-Distribution
A major stage of radio interferometric data processing is calibration or the
estimation of systematic errors in the data and the correction for such errors.
A stochastic error (noise) model is assumed, and in most cases, this underlying
model is assumed to be Gaussian. However, outliers in the data due to
interference or due to errors in the sky model would have adverse effects on
processing based on a Gaussian noise model. Most of the shortcomings of
calibration such as the loss in flux or coherence, and the appearance of
spurious sources, could be attributed to the deviations of the underlying noise
model. In this paper, we propose to improve the robustness of calibration by
using a noise model based on Student's t distribution. Student's t noise is a
special case of Gaussian noise when the variance is unknown. Unlike Gaussian
noise model based calibration, traditional least squares minimization would not
directly extend to a case when we have a Student's t noise model. Therefore, we
use a variant of the Expectation Maximization (EM) algorithm, called the
Expectation-Conditional Maximization Either (ECME) algorithm when we have a
Student's t noise model and use the Levenberg-Marquardt algorithm in the
maximization step. We give simulation results to show the robustness of the
proposed calibration method as opposed to traditional Gaussian noise model
based calibration, especially in preserving the flux of weaker sources that are
not included in the calibration model.Comment: MNRAS accepte
Reduced Ambiguity Calibration for LOFAR
Interferometric calibration always yields non unique solutions. It is
therefore essential to remove these ambiguities before the solutions could be
used in any further modeling of the sky, the instrument or propagation effects
such as the ionosphere. We present a method for LOFAR calibration which does
not yield a unitary ambiguity, especially under ionospheric distortions. We
also present exact ambiguities we get in our solutions, in closed form. Casting
this as an optimization problem, we also present conditions for this approach
to work. The proposed method enables us to use the solutions obtained via
calibration for further modeling of instrumental and propagation effects. We
provide extensive simulation results on the performance of our method.
Moreover, we also give cases where due to degeneracy, this method fails to
perform as expected and in such cases, we suggest exploiting diversity in time,
space and frequency.Comment: Draft version. Final version published on 10 April 201
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