53 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
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