77 research outputs found
Imaging and uncertainty quantification in radio astronomy via convex optimization : when precision meets scalability
Upcoming radio telescopes such as the Square Kilometre Array (SKA) will provide sheer amounts
of data, allowing large images of the sky to be reconstructed at an unprecedented resolution and
sensitivity over thousands of frequency channels. In this regard, wideband radio-interferometric
imaging consists in recovering a 3D image of the sky from incomplete and noisy Fourier data, that
is a highly ill-posed inverse problem. To regularize the inverse problem, advanced prior image
models need to be tailored. Moreover, the underlying algorithms should be highly parallelized to
scale with the vast data volumes provided and the Petabyte image cubes to be reconstructed for
SKA. The research developed in this thesis leverages convex optimization techniques to achieve
precise and scalable imaging for wideband radio interferometry and further assess the degree of
confidence in particular 3D structures present in the reconstructed cube.
In the context of image reconstruction, we propose a new approach that decomposes the image
cube into regular spatio-spectral facets, each is associated with a sophisticated hybrid prior image
model. The approach is formulated as an optimization problem with a multitude of facet-based
regularization terms and block-specific data-fidelity terms. The underpinning algorithmic structure benefits from well-established convergence guarantees and exhibits interesting functionalities
such as preconditioning to accelerate the convergence speed. Furthermore, it allows for parallel processing of all data blocks and image facets over a multiplicity of CPU cores, allowing the
bottleneck induced by the size of the image and data cubes to be efficiently addressed via parallelization. The precision and scalability potential of the proposed approach are confirmed through
the reconstruction of a 15 GB image cube of the Cyg A radio galaxy.
In addition, we propose a new method that enables analyzing the degree of confidence in
particular 3D structures appearing in the reconstructed cube. This analysis is crucial due to the
high ill-posedness of the inverse problem. Besides, it can help in making scientific decisions on
the structures under scrutiny (e.g., confirming the existence of a second black hole in the Cyg A
galaxy). The proposed method is posed as an optimization problem and solved efficiently with
a modern convex optimization algorithm with preconditioning and splitting functionalities. The
simulation results showcase the potential of the proposed method to scale to big data regimes
Advanced sparse optimization algorithms for interferometric imaging inverse problems in astronomy
In the quest to produce images of the sky at unprecedented resolution with high
sensitivity, new generation of astronomical interferometers have been designed. To
meet the sensing capabilities of these instruments, techniques aiming to recover the
sought images from the incompletely sampled Fourier domain measurements need to
be reinvented. This goes hand-in-hand with the necessity to calibrate the measurement modulating unknown effects, which adversely affect the image quality, limiting
its dynamic range. The contribution of this thesis consists in the development of
advanced optimization techniques tailored to address these issues, ranging from radio
interferometry (RI) to optical interferometry (OI).
In the context of RI, we propose a novel convex optimization approach for full polarization imaging relying on sparsity-promoting regularizations. Unlike standard RI
imaging algorithms, our method jointly solves for the Stokes images by enforcing the
polarization constraint, which imposes a physical dependency between the images.
These priors are shown to enhance the imaging quality via various performed numerical studies. The proposed imaging approach also benefits from its scalability to handle
the huge amounts of data expected from the new instruments. When it comes to deal
with the critical and challenging issues of the direction-dependent effects calibration,
we further propose a non-convex optimization technique that unifies calibration and
imaging steps in a global framework, in which we adapt the earlier developed imaging
method for the imaging step. In contrast to existing RI calibration modalities, our
method benefits from well-established convergence guarantees even in the non-convex
setting considered in this work and its efficiency is demonstrated through several
numerical experiments.
Last but not least, inspired by the performance of these methodologies and drawing
ideas from them, we aim to solve image recovery problem in OI that poses its own
set of challenges primarily due to the partial loss of phase information. To this end,
we propose a sparsity regularized non-convex optimization algorithm that is equipped
with convergence guarantees and is adaptable to both monochromatic and hyperspectral OI imaging. We validate it by presenting the simulation results
Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): when precision meets scalability
Upcoming radio interferometers are aiming to image the sky at new levels of
resolution and sensitivity, with wide-band image cubes reaching close to the
Petabyte scale for SKA. Modern proximal optimization algorithms have shown a
potential to significantly outperform CLEAN thanks to their ability to inject
complex image models to regularize the inverse problem for image formation from
visibility data. They were also shown to be scalable to large data volumes
thanks to a splitting functionality enabling the decomposition of data into
blocks, for parallel processing of block-specific data-fidelity terms of the
objective function. In this work, the splitting functionality is further
exploited to decompose the image cube into spatio-spectral facets, and enable
parallel processing of facet-specific regularization terms in the objective.
The resulting Faceted HyperSARA algorithm is implemented in MATLAB (code
available on GitHub). Simulation results on synthetic image cubes confirm that
faceting can provide a major increase in scalability at no cost in imaging
quality. A proof-of-concept reconstruction of a 15 GB image of Cyg A from 7.4
GB of VLA data, utilizing 496 CPU cores on a HPC system for 68 hours, confirms
both scalability and a quantum jump in imaging quality from CLEAN. Assuming
slow spectral slope of Cyg A, we also demonstrate that Faceted HyperSARA can be
combined with a dimensionality reduction technique, enabling utilizing only 31
CPU cores for 142 hours to form the Cyg A image from the same data, while
preserving reconstruction quality. Cyg A reconstructed cubes are available
online
Scalable precision wide-field imaging in radio interferometry: I. uSARA validated on ASKAP data
As Part I of a paper series showcasing a new imaging framework, we consider
the recently proposed unconstrained Sparsity Averaging Reweighted Analysis
(uSARA) optimisation algorithm for wide-field, high-resolution, high-dynamic
range, monochromatic intensity imaging. We reconstruct images from real
radio-interferometric observations obtained with the Australian Square
Kilometre Array Pathfinder (ASKAP) and present these results in comparison to
the widely-used, state-of-the-art imager WSClean. Selected fields come from the
ASKAP Early Science and Evolutionary Map of the Universe (EMU) Pilot surveys
and contain several complex radio sources: the merging cluster system Abell
3391-95, the merging cluster SPT-CL 2023-5535, and many extended, or bent-tail,
radio galaxies, including the X-shaped radio galaxy PKS 2014-558 and the
``dancing ghosts'', known collectively as PKS 2130-538. The modern framework
behind uSARA utilises parallelisation and automation to solve for the w-effect
and efficiently compute the measurement operator, allowing for wide-field
reconstruction over the full field-of-view of individual ASKAP beams (up to 3.3
deg each). The precision capability of uSARA produces images with both
super-resolution and enhanced sensitivity to diffuse components, surpassing
traditional CLEAN algorithms which typically require a compromise between such
yields. Our resulting monochromatic uSARA-ASKAP images of the selected data
highlight both extended, diffuse emission and compact, filamentary emission at
very high resolution (up to 2.2 arcsec), revealing never-before-seen structure.
Here we present a validation of our uSARA-ASKAP images by comparing the
morphology of reconstructed sources, measurements of diffuse flux, and spectral
index maps with those obtained from images made with WSClean.Comment: Accepted for publication in MNRA
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