Radio Astronomical Image Research Articles

Radio-astronomical image reconstruction with a conditional denoising diffusion model

Context. Reconstructing sky models from dirty radio images for accurate source extraction, including source localization and flux estimation, is a complex yet critical task, and has important applications in galaxy evolution studies at high redshift, particularly in deep extragalactic fields using for example the Atacama Large Millimetre Array (ALMA). With the development of large-scale projects, such as the Square Kilometre Array (SKA), we anticipate the need for more advanced source-extraction methods. Existing techniques, such as CLEAN and PyBDSF, currently struggle to effectively extract faint sources, highlighting the necessity for the development of more precise and robust methods. Aims. The success of the source-extraction process critically depends on the quality and accuracy of image reconstruction. As the imaging process represents an “information-lossy” operator, the reconstruction is characterized by uncertainty. The current study proposes the application of stochastic neural networks for the direct reconstruction of sky models from “dirty” images. This approach allows us to localize radio sources and to determine their fluxes with corresponding uncertainties, providing a potential advancement in the field of radio-source characterization. Methods. We used a dataset of 10 164 images simulated with the CASA tool based on the ALMA Cycle 5.3 antenna configuration. We applied conditional denoising diffusion probabilistic models (DDPMs) to directly reconstruct sky models from these dirty images, and then processed these models using Photutils to extract the coordinates and fluxes of the sources. To test the robustness of the proposed model, which was trained on a fixed water vapor value, we examined its performance under varying levels of water vapor. Results. We demonstrate that the proposed approach is state of the art in terms of source localisation, achieving over 90% completeness at a signal-to-noise ratio (S/N) of as low as 2. Additionally, the described method offers an inherent measure of prediction reliability thanks to the stochastic nature of the chosen model. In terms of flux estimation, the proposed model surpasses PyBDSF in terms of performance, accurately extracting fluxes for 96% of the sources in the test set, a notable improvement over the 57% achieved by CLEAN+ PyBDSF. Conclusions. Conditional DDPMs are a powerful tool for image-to-image translation, yielding accurate and robust characterization of radio sources, and outperforming existing methodologies. While this study underscores the significant potential of DDPMs for applications in radio astronomy, we also acknowledge certain limitations that accompany their use, and suggest directions for further refinement and research.

射电天文图像重建算法研究综述

射电天文图像重建算法研究综述

A parallel and automatically tuned algorithm for multispectral image deconvolution

ABSTRACT In the era of big data, radio astronomical image reconstruction algorithms are challenged to estimate clean images given limited computing resources and time. This article is driven by the need for large-scale image reconstruction for the future Square Kilometre Array (SKA), which will become in the next decades the largest low and intermediate frequency radio telescope in the world. This work proposes a scalable wide-band deconvolution algorithm called MUFFIN, which stands for ‘MUlti Frequency image reconstruction For radio INterferometry’. MUFFIN estimates the sky images in various frequency bands, given the corresponding dirty images and point spread functions. The reconstruction is achieved by minimizing a data fidelity term and joint spatial and spectral sparse analysis regularization terms. It is consequently non-parametric w.r.t. the spectral behaviour of radio sources. MUFFIN algorithm is endowed with a parallel implementation and an automatic tuning of the regularization parameters, making it scalable and well suited for big data applications such as SKA. Comparisons between MUFFIN and the state-of-the-art wide-band reconstruction algorithm are provided.

Extracting Filaments Based on Morphology Components Analysis from Radio Astronomical Images

Filaments are a type of wide-existing astronomical structure. It is a challenge to separate filaments from radio astronomical images, because their radiation is usually weak. What is more, filaments often mix with bright objects, e.g., stars, which makes it difficult to separate them. In order to extract filaments, A. Men’shchikov proposed a method “getfilaments” to find filaments automatically. However, the algorithm removed tiny structures by counting connected pixels number simply. Removing tiny structures based on local information might remove some part of the filaments because filaments in radio astronomical image are usually weak. In order to solve this problem, we applied morphology components analysis (MCA) to process each singe spatial scale image and proposed a filaments extraction algorithm based on MCA. MCA uses a dictionary whose elements can be wavelet translation function, curvelet translation function, or ridgelet translation function to decompose images. Different selection of elements in the dictionary can get different morphology components of the spatial scale image. By using MCA, we can get line structure, gauss sources, and other structures in spatial scale images and exclude the components that are not related to filaments. Experimental results showed that our proposed method based on MCA is effective in extracting filaments from real radio astronomical images, and images processed by our method have higher peak signal-to-noise ratio (PSNR).

Radio astronomical image formation using constrained least squares and Krylov subspaces

Image formation for radio astronomy can be defined as estimating the spatial power distribution of celestial sources over the sky, given an array of antennas. One of the challenges with image formation is that the problem becomes ill-posed as the number of pixels becomes large. The introduction of constraints that incorporate a-priori knowledge is crucial. In this paper we show that in addition to non-negativity, the magnitude of each pixel in an image is also bounded from above. Indeed, the classical "dirty image" is an upper bound, but a much tighter upper bound can be formed from the data using array processing techniques. This formulates image formation as a least squares optimization problem with inequality constraints. We propose to solve this constrained least squares problem using active set techniques, and the steps needed to implement it are described. It is shown that the least squares part of the problem can be efficiently implemented with Krylov subspace based techniques, where the structure of the problem allows massive parallelism and reduced storage needs. The performance of the algorithm is evaluated using simulations.