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Register a new userHigh accuracy wide field imaging method in radio interferometry
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With the development of modern radio interferometers, wide-field continuum surveys have been planned and undertaken, for which accurate wide-field imaging methods are essential. Based on the widely-used W-stacking method, we propose a new wide-field imaging algorithm that can synthesize visibility data from a model of the sky brightness via degridding, able to construct dirty maps from measured visibility data via gridding. Results carry the smallest approximation error yet achieved relative to the exact calculation involving the direct Fourier transform. In contrast to the original W-stacking method, the new algorithm performs least-misfit optimal gridding (and degridding) in all three directions, and is capable of achieving much higher accuracy than is feasible with the original algorithm. In particular, accuracy at the level of single precision arithmetic is readily achieved by choosing a least-misfit convolution function of width W=7 and an image cropping parameter of x_0=0.25. If the accuracy required is only that attained by the original W-stacking method, the computational cost for both the gridding and FFT steps can be substantially reduced using the proposed method by making an appropriate choice of the width and image cropping parameters.
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We investigate the imaging performance of an interferometric array in the case of wide field, high resolution, narrow band, snapshot imaging. We find that, when uv-cell sizes are sufficiently small (ie. image sizes are sufficiently large), each instantaneous visibility record is gridded into its own uv-cell. This holds even for dense arrays, like the core of the next generation VLA. In this particular, application, Uniform weighting of the gridded visibilities approaches Natural weighting, with its often deleterious consequences on the resulting synthesized beam. For a core-dominated array, we show that the resulting image noise is highly correlated on scales comparable to the spatial frequencies of the core baselines. In general, this study accentuates the fact that, for imaging applications that require high resolution (Plains array and greater), many of the core antennas can be employed as a separate subarray for low resolution science, without sacrificing the quality of the high resolution science.
We consider the probe of astrophysical signals through radio interferometers with small field of view and baselines with non-negligible and constant component in the pointing direction. In this context, the visibilities measured essentially identify with a noisy and incomplete Fourier coverage of the product of the planar signals with a linear chirp modulation. In light of the recent theory of compressed sensing and in the perspective of defining the best possible imaging techniques for sparse signals, we analyze the related spread spectrum phenomenon and suggest its universality relative to the sparsity dictionary. Our results rely both on theoretical considerations related to the mutual coherence between the sparsity and sensing dictionaries, as well as on numerical simulations.
In radio interferometry imaging, the gridding procedure of convolving visibilities with a chosen gridding function is necessary to transform visibility values into uniformly sampled grid points. We propose here a parameterised family of least-misfit gridding functions which minimise an upper bound on the difference between the DFT and FFT dirty images for a given gridding support width and image cropping ratio. When compared with the widely used spheroidal function with similar parameters, these provide more than 100 times better alias suppression and RMS misfit reduction over the usable dirty map. We discuss how appropriate parameter selection and tabulation of these functions allow for a balance between accuracy, computational cost and storage size. Although it is possible to reduce the errors introduced in the gridding or degridding process to the level of machine precision, accuracy comparable to that achieved by CASA requires only a lookup table with 300 entries and a support width of 3, allowing for a greatly reduced computation cost for a given performance.
CLEAN, the commonly employed imaging algorithm in radio interferometry, suffers from a number of shortcomings: in its basic version it does not have the concept of diffuse flux, and the common practice of convolving the CLEAN components with the CLEAN beam erases the potential for super-resolution; it does not output uncertainty information; it produces images with unphysical negative flux regions; and its results are highly dependent on the so-called weighting scheme as well as on any human choice of CLEAN masks to guiding the imaging. Here, we present the Bayesian imaging algorithm resolve which solves the above problems and naturally leads to super-resolution. We take a VLA observation of Cygnus~A at four different frequencies and image it with single-scale CLEAN, multi-scale CLEAN and resolve. Alongside the sky brightness distribution resolve estimates a baseline-dependent correction function for the noise budget, the Bayesian equivalent of weighting schemes. We report noise correction factors between 0.4 and 429. The enhancements achieved by resolve come at the cost of higher computational effort.
GravityCam is a new concept of ground-based imaging instrument capable of delivering significantly sharper images from the ground than is normally possible without adaptive optics. Advances in optical and near infrared imaging technologies allow images to be acquired at high speed without significant noise penalty. Aligning these images before they are combined can yield a 2.5 to 3 fold improvement in image resolution. By using arrays of such detectors, survey fields may be as wide as the telescope optics allows. Consequently, GravityCam enables both wide-field high-resolution imaging and high-speed photometry. We describe the instrument and detail its application to provide demographics of planets and satellites down to Lunar mass (or even below) across the Milky Way. GravityCam is also suited to improve the quality of weak shear studies of dark matter distribution in distant clusters of galaxies and multiwavelength follow-ups of background sources that are strongly lensed by galaxy clusters. The photometric data arising from an extensive microlensing survey will also be useful for asteroseismology studies, while GravityCam can be used to monitor fast multiwavelength flaring in accreting compact objects, and promises to generate a unique data set on the population of the Kuiper belt and possibly the Oort cloud.
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