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Register a new userRobust dimensionality reduction for interferometric imaging of Cygnus A
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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 method comprises a simple weighted, subsampled discrete Fourier transform of the dirty image. Additionally, we show that an alternative gridding-based reduction method works well for target data sizes of the same order as the image size. We reconstruct images from well-calibrated VLA data to showcase the robustness of our proposed method down to very low data sizes in a real data setting. We show through comparisons with the conventional reduction method of time- and frequency-averaging, that our proposed method produces more accurate reconstructions while reducing data size much further, and is particularly robust when data sizes are aggressively reduced to low fractions of the image size. $mathrm{R}_{mathrm{sing}}$ can function in a block-wise fashion, and could be used in the future to process incoming data by blocks in real-time, thus opening up the possibility of performing on-line imaging as the data are being acquired. MATLAB code for the proposed dimensionality reduction method is available on GitHub.
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High resolution galaxy spectra contain much information about galactic physics, but the high dimensionality of these spectra makes it difficult to fully utilize the information they contain. We apply variational autoencoders (VAEs), a non-linear dimensionality reduction technique, to a sample of spectra from the Sloan Digital Sky Survey. In contrast to Principal Component Analysis (PCA), a widely used technique, VAEs can capture non-linear relationships between latent parameters and the data. We find that a VAE can reconstruct the SDSS spectra well with only six latent parameters, outperforming PCA with the same number of components. Different galaxy classes are naturally separated in this latent space, without class labels having been given to the VAE. The VAE latent space is interpretable because the VAE can be used to make synthetic spectra at any point in latent space. For example, making synthetic spectra along tracks in latent space yields sequences of realistic spectra that interpolate between two different types of galaxies. Using the latent space to find outliers may yield interesting spectra: in our small sample, we immediately find unusual data artifacts and stars misclassified as galaxies. In this exploratory work, we show that VAEs create compact, interpretable latent spaces that capture non-linear features of the data. While a VAE takes substantial time to train (~1 day for 48000 spectra), once trained, VAEs can enable the fast exploration of large astronomical data sets.
Next generation radio telescopes, like the Square Kilometre Array, will acquire an unprecedented amount of data for radio astronomy. The development of fast, parallelisable or distributed algorithms for handling such large-scale data sets is of prime importance. Motivated by this, we investigate herein a convex optimisation algorithmic structure, based on primal-dual forward-backward iterations, for solving the radio interferometric imaging problem. It can encompass any convex prior of interest. It allows for the distributed processing of the measured data and introduces further flexibility by employing a probabilistic approach for the selection of the data blocks used at a given iteration. We study the reconstruction performance with respect to the data distribution and we propose the use of nonuniform probabilities for the randomised updates. Our simulations show the feasibility of the randomisation given a limited computing infrastructure as well as important computational advantages when compared to state-of-the-art algorithmic structures.
High-resolution astronomical imaging at sub-GHz radio frequencies has been available for more than 15 years, with the VLA at 74 and 330 MHz, and the GMRT at 150, 240, 330 and 610 MHz. Recent developments include wide-bandwidth upgrades for VLA and GMRT, and commissioning of the aperture-array-based, multi-beam telescope LOFAR. A common feature of these telescopes is the necessity to deconvolve the very many detectable sources within their wide fields-of-view and beyond. This is complicated by gain variations in the radio signal path that depend on viewing direction. One such example is phase errors due to the ionosphere. Here I discuss the inner workings of SPAM, a set of AIPS-based data reduction scripts in Python that includes direction-dependent calibration and imaging. Since its first version in 2008, SPAM has been applied to many GMRT data sets at various frequencies. Many valuable lessons were learned, and translated into various SPAM software modifications. Nowadays, semi-automated SPAM data reduction recipes can be applied to almost any GMRT data set, yielding good quality continuum images comparable with (or often better than) hand-reduced results. SPAM is currently being migrated from AIPS to CASA with an extension to handle wide bandwidths. This is aimed at providing users of the VLA low-band system and the upcoming wide-bandwidth GMRT with the necessary data reduction tools.
We present the results of the fifth Interferometric Imaging Beauty Contest. The contest consists in blind imaging of test data sets derived from model sources and distributed in the OIFITS format. Two scenarios of imaging with CHARA/MIRC-6T were offered for reconstruction: imaging a T Tauri disc and imaging a spotted red supergiant. There were eight different teams competing this time: Monnier with the software package MACIM; Hofmann, Schertl and Weigelt with IRS; Thiebaut and Soulez with MiRA ; Young with BSMEM; Mary and Vannier with MIROIRS; Millour and Vannier with independent BSMEM and MiRA entries; Rengaswamy with an original method; and Elias with the radio-astronomy package CASA. The contest model images, the data delivered to the contestants and the rules are described as well as the results of the image reconstruction obtained by each method. These results are discussed as well as the strengths and limitations of each algorithm.
We study the impact of the spread spectrum effect in radio interferometry on the quality of image reconstruction. This spread spectrum effect will be induced by the wide field-of-view of forthcoming radio interferometric telescopes. The resulting chirp modulation improves the quality of reconstructed interferometric images by increasing the incoherence of the measurement and sparsity dictionaries. We extend previous studies of this effect to consider the more realistic setting where the chirp modulation varies for each visibility measurement made by the telescope. In these first preliminary results, we show that for this setting the quality of reconstruction improves significantly over the case without chirp modulation and achieves almost the reconstruction quality of the case of maximal, constant chirp modulation.
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