We analyse the accuracy of radio interferometric gridding of visibilities with the aim to quantify the Epoch of Reionization (EoR) 21-cm power spectrum bias caused by gridding, ultimately to determine the suitability of different imaging algorithms and gridding settings for 21-cm power spectrum analysis. We simulate realistic LOFAR data, and construct power spectra with convolutional gridding and w-stacking, w-projection, image domain gridding and without w-correction. These are compared against directly Fourier transformed data. The influence of oversampling, kernel size, w-quantization, kernel windowing function and image padding are quantified. The gridding excess power is measured with a foreground subtraction strategy, for which foregrounds have been subtracted using Gaussian progress regression, as well as with a foreground avoidance strategy.
Constructing a power spectrum that has a bias significantly lower compared to the expected EoR signals is possible with the tested methods, but requires a kernel oversampling factor > 4000 and, when using w-correction, > 500 w-quantization levels. These values are higher than typical values used for imaging, but are computationally feasible. The kernel size and padding factor parameters are less crucial. Among the tested methods, image domain gridding shows the highest accuracy with the lowest imaging time.
LOFAR 21-cm power spectrum results are not affected by gridding. Image domain gridding is overall the most suitable algorithm for 21-cm EoR experiments, including for future SKA EoR analyses. Nevertheless, convolutional gridding with tuned parameters results in sufficient accuracy. This holds also for w-stacking for wide-field imaging. The w-projection algorithm is less suitable because of the kernel oversampling requirements, and a faceting approach is unsuitable due to the resulting spatial discontinuities.
Convolutional Gridding is a technique (algorithm) extensively used in Radio Interferometric Image Synthesis for fast inversion of functions sampled with irregular intervals on the Fourier plane. In this thesis, we propose some modifications to the technique to execute faster on a GPU. These modifications give rise to \textit{Hybrid Gridding} and \textit{Pruned NN Interpolation}, which take advantage of the oversampling of the Gridding Convolutional Function in Convolutional Gridding to try to make gridding faster with no reduction in the quality of the output. Our experiments showed that given…
Reconstruction of the sky brightness measured by radio interferometers is typically achieved through gridding techniques, or histograms in spatial Fourier space. For Epoch of Reionisation (EoR) 21 cm power spectrum measurements, extreme levels of gridding resolution are required to reduce spectral contamination, as explored in other works. However, the role of the shape of the Fourier space spreading function, or kernel, also has consequences in reconstructed power spectra. We decompose the instrumental Murchison Widefield Array (MWA) beam into a series of Gaussians and simulate the effects of…
We present a new algorithm to perform wide-field radio interferometric image reconstruction, with exact non-coplanar correction, that scales to big-data. This algorithm allows us to image 2 billion visibilities on 50 nodes of a computing cluster for a 25 by 25 degree field of view, in a little over an hour. We build on the recently developed distributed $w$-stacking $w$-projection hybrid algorithm, extending it to include a new distributed degridding algorithm that balances the computational load of the $w$-projection gridding kernels. The implementation of our algorithm is made publicly avail…
After several generations of interferometers in radioastronomy, wide-field imaging at high angular resolution is today a major goal for trying to match optical wide-field performances. All the radio-interferometric, wide-field imaging methods currently belong to the mosaicking family. Based on a 30 years old, original idea from Ekers & Rots, we aim at proposing an alternate formalism. Starting from their ideal case, we successively evaluate the impact of the standard ingredients of interferometric imaging. A comparison with standard nonlinear mosaicking shows that both processing schemes are n…
The analysis of astronomical interferometric data is often performed on the images obtained after deconvolution of the interferometer's point spread function (PSF). This strategy can be understood (especially for cases of sparse arrays) as fitting models to models, since the deconvolved images are already non-unique model representations of the actual data (i.e., the visibilities). Indeed, the interferometric images may be affected by visibility gridding, weighting schemes (e.g., natural vs. uniform), and the particulars of the (non-linear) deconvolution algorithms. Fitting models to the direc…
The standard imaging algorithm for interferometric radio data, CLEAN, is optimal for point source observations, but suboptimal for diffuse emission. Recently, RESOLVE, a new Bayesian algorithm has been developed, which is ideal for extended source imaging. Unfortunately, RESOLVE is computationally very expensive. In this paper we present fastRESOLVE, a modification of RESOLVE based on an approximation of the interferometric likelihood that allows us to avoid expensive gridding routines and consequently gain a factor of roughly 100 in computation time. Furthermore, we include a Bayesian estimat…
Radio interferometers do not measure the sky brightness distribution directly but rather a modified Fourier transform of it. Imaging algorithms, thus, need a computational representation of the linear measurement operator and its adjoint, irrespective of the specific chosen imaging algorithm. In this paper, we present a C++ implementation of the radio interferometric measurement operator for wide-field measurements which is based on "improved $w$-stacking". It can provide high accuracy (down to $\approx 10^{-12}$), is based on a new gridding kernel which allows smaller kernel support for given…
Modern radio telescopes, such as the Square Kilometre Array (SKA), will probe the radio sky over large fields-of-view, which results in large w-modulations of the sky image. This effect complicates the relationship between the measured visibilities and the image under scrutiny. In algorithmic terms, it gives rise to massive memory and computational time requirements. Yet, it can be a blessing in terms of reconstruction quality of the sky image. In recent years, several works have shown that large w-modulations promote the spread spectrum effect. Within the compressive sensing framework, this e…
Extremely high data rates expected in next-generation radio interferometers necessitate a fast and robust way to process measurements in a big data context. Dimensionality reduction can alleviate computational load needed to process these data, in terms of both computing speed and memory usage. In this article, we present image reconstruction results from highly reduced radio-interferometric data, following our previously proposed data dimensionality reduction method, $\mathrm{R}_{\mathrm{sing}}$, based on studying the distribution of the singular values of the measurement operator. This metho…
Next-generation radio interferometers, such as the Square Kilometre Array (SKA), will revolutionise our understanding of the universe through their unprecedented sensitivity and resolution. However, to realise these goals significant challenges in image and data processing need to be overcome. The standard methods in radio interferometry for reconstructing images, such as CLEAN, have served the community well over the last few decades and have survived largely because they are pragmatic. However, they produce reconstructed inter\-ferometric images that are limited in quality and scalability fo…
Data dimensionality reduction in radio interferometry can provide savings of computational resources for image reconstruction through reduced memory footprints and lighter computations per iteration, which is important for the scalability of imaging methods to the big data setting of the next-generation telescopes. This article sheds new light on dimensionality reduction from the perspective of compressed sensing theory and studies its interplay with imaging algorithms designed in the context of convex optimization. We propose a post-gridding linear data embedding to the space spanned by the l…
The requirements on space missions designed to study Terrestrial exoplanets are discussed. We then investigate whether the design of such a mission, specifically the Darwin nulling interferometer, can be carried out in a simplified scenario. The key element here is accepting somewhat higher levels of stellar leakage. We establish detailed requirements resulting from the scientific rationale for the mission, and calculate detailed parameters for the stellar suppression required to achieve those requirements. We do this utilizing the Darwin input catalogue. The dominating noise source for most t…
The channel-to-channel migration of radio interferometric baselines for the same antenna separation causes a flat spectrum source that should have remained in the zeroth delay (line-of-sight) mode to become centered around a higher mode - the geometric delay for that particular antenna separation with a spread (spill-over) of the order of reciprocal bandwidth. While in principle an errorless gridding interpolation can remove inter-channel migration, intra-channel baseline migration exists due to the non-zero width (resolution) of the instrument's spectral channel arising from the finite-period…
Radio astronomy is transitioning to a big-data era due to the emerging generation of radio interferometric (RI) telescopes, such as the Square Kilometre Array (SKA), which will acquire massive volumes of data. In this article we review methods proposed recently to resolve the ill-posed inverse problem of imaging the raw visibilities acquired by RI telescopes in the big-data scenario. We focus on the recently proposed online reconstruction method [4] and the considerable savings in data storage requirements and computational cost that it yields.
AMIGO is a first-generation Astrodynamical Middle-frequency Interferometric GW Observatory. The scientific goals of AMIGO are: to bridge the spectra gap between first-generation high-frequency and low-frequency GW sensitivities; to detect intermediate mass BH coalescence; to detect inspiral phase and predict time of binary black hole coalescence together with neutron star coalescence for ground interferometers; to detect compact binary inspirals for studying stellar evolution and galactic population. The mission concept is to use time delay interferometry for a nearly triangular formation of 3…
In the context of next generation radio telescopes, like the Square Kilometre Array, the efficient processing of large-scale datasets is extremely important. Convex optimisation tasks under the compressive sensing framework have recently emerged and provide both enhanced image reconstruction quality and scalability to increasingly larger data sets. We focus herein mainly on scalability and propose two new convex optimisation algorithmic structures able to solve the convex optimisation tasks arising in radio-interferometric imaging. They rely on proximal splitting and forward-backward iteration…
The residual gain errors add to the systematics of the radio interferometric observations. In case of the high dynamic range observations, these systematic effects dominates over the thermal noise of the observation. In this work, we investigate the effect of time-correlated residual gain errors in the estimation of the power spectrum of the sky brightness distribution in high dynamic range observations. Particularly, we discuss a methodology to estimate the bias in the power spectrum estimator of the redshifted 21-cm signal from neutral hydrogen in the presence of bright extragalactic compact…
Point-spread function (PSF) estimation in spatially undersampled images is challenging because large pixels average fine-scale spatial information. This is problematic when fine-resolution details are necessary, as in optimal photometry where knowledge of the illumination pattern beyond the native spatial resolution of the image may be required. Here, we introduce a method of PSF reconstruction where point sources are artificially sampled beyond the native resolution of an image and combined together via stacking to return a finely sampled estimate of the PSF. This estimate is then deconvolved…
Interferometry with phased-array feeds (PAFs) presents new calibration challenges in comparison with single-pixel feeds. In particular, temporal instability of the compound beam patterns due to element gain drifts (EGDs) can produce calibration artefacts in interferometric images. To translate imaging dynamic range requirements into PAF hardware and calibration requirements, we must learn to relate EGD levels to imaging artefact levels. We present a MeqTrees-based simulations framework that addresses this problem, and apply it to the APERTIF prototype currently in development for the WSRT.
The emerging generation of radio interferometric (RI) telescopes, such as the Square Kilometre Array (SKA), will acquire massive volumes of data and transition radio astronomy to a big-data era. The ill-posed inverse problem of imaging the raw visibilities acquired by RI telescopes will become significantly more computationally challenging, particularly in terms of data storage and computational cost. Current RI imaging methods, such as CLEAN, its variants, and compressive sensing approaches (sparse regularisation), have yielded excellent reconstruction fidelity. However, scaling these methods…
Observations of the Sun at millimeter and submillimeter wavelengths offer a unique probe into the structure, dynamics, and heating of the chromosphere; the structure of sunspots; the formation and eruption of prominences and filaments; and energetic phenomena such as jets and flares. High-resolution observations of the Sun at millimeter and submillimeter wavelengths are challenging due to the intense, extended, low- contrast, and dynamic nature of emission from the quiet Sun, and the extremely intense and variable nature of emissions associated with energetic phenomena. The Atacama Large Milli…
Theia is an astrometric mission proposed to ESA in 2014 for which one of the scientific objectives is detecting Earth-like exoplanets in the habitable zone of nearby solar-type stars. This objective requires the capability to measure stellar centroids at the precision of 1e-5 pixel. Current state-of-the-art methods for centroid estimation have reached a precision of about 3e-5 pixel at two times Nyquist sampling, this was shown at the JPL by the VESTA experiment. A metrology system was used to calibrate intra and inter pixel quantum efficiency variations in order to correct pixelation errors. …
POLISH'ing the Sky: Wide-Field and High-Dynamic Range Interferometric Image Reconstruction with Application to Strong Lens Discovery
Department of Computing and Mathematical Sciences, California Institute of Technology (Caltech), Pasadena, CA, USA（计算与数学科学系，加州理工学院（Caltech），帕萨迪纳，CA，美国）；Center for Astrophysics ∣ \mid Harvard & Smithsonian, Cambridge, MA, USA（天体物理中心，哈佛与史密松尼天体物理研究中心，剑桥，MA，美国）
With the LIGO announcement of the first direct detection of gravitational waves (GWs), the GW Astronomy was formally ushered into our age. After one-hundred years of theoretical investigation and fifty years of experimental endeavor, this is a historical landmark not just for physics and astronomy, but also for industry and manufacturing. The challenge and opportunity for industry is precision and innovative manufacturing in large size - production of large and homogeneous optical components, optical diagnosis of large components, high reflectance dielectric coating on large mirrors, manufactu…
Epoch of Reionization data analysis requires unprecedented levels of accuracy in radio interferometer pipelines. We have developed an imaging power spectrum analysis to meet these requirements and generate robust 21 cm EoR measurements. In this work, we build a signal path framework to mathematically describe each step in the analysis, from data reduction in the FHD package to power spectrum generation in the $\varepsilon$ppsilon package. In particular, we focus on the distinguishing characteristics of FHD/$\varepsilon$ppsilon: highly accurate spectral calibration, extensive data verification …
