Radio astronomy | EPFL Graph Search

Radio astronomy is a subfield of astronomy that studies celestial objects at radio frequencies. The first detection of radio waves from an astronomical object was in 1933, when Karl Jansky at Bell Telephone Laboratories reported radiation coming from the Milky Way. Subsequent observations have identified a number of different sources of radio emission. These include stars and galaxies, as well as entirely new classes of objects, such as radio galaxies, quasars, pulsars, and masers. The discovery of the cosmic microwave background radiation, regarded as evidence for the Big Bang theory, was made through radio astronomy. Radio astronomy is conducted using large radio antennas referred to as radio telescopes, that are either used singularly, or with multiple linked telescopes utilizing the techniques of radio interferometry and aperture synthesis. The use of interferometry allows radio astronomy to achieve high angular resolution, as the resolving power of an interferometer is set by th

Development and evaluation of galaxy shape measurement algorithms for radio interferometric data

Vasileios Angelopoulos

The purpose of this Thesis is to develop, test, and characterize different models attempting to tackle the problem of measurement of galaxy shapes applied in interferometric observations. Shape measurement is a tool for estimating the underlying shear due to weak gravitational lensing by large-scale foreground matter distributions. The recent enormous progress in radio interferometric imaging during the last years, with projects such as MeerKAT, ASKAP, and in future SKA, motivates the development of advanced algorithms that utilize radio observations for this task. In most of these algorithms, the main disadvantage is that the proposed models' parameters have to be estimated in advance, assuming certain prior knowledge on the form of the objects. Our motivation is to develop and evaluate frameworks that do not require significant prior information on this form to make accurate measurements.To achieve this goal, we move in two implementation directions. In the first one, we are based on an existing approach that applies an object decomposition procedure using a dictionary of shapelet functions. Our proposal goes some steps forward trying to estimate this decomposition's key size parameter from a model built on advanced regularization. More specifically, we create an over-redundant dictionary formed as the concatenation of several groups of orthogonal shapelet basis functions with different parameters, and we use structured sparsity penalties to estimate the size parameter of the best group. As an alternative development strategy, we form an algorithm that employs multi-resolution least-squares analysis, which attempts to identify the size value that minimizes the relative residual in the fitting.The second path, instead of making the shape estimation directly on radio interferometric data, performs image reconstruction from the visibilities and measures the ellipticity of the objects in the resulting images. For this purpose, we adopt a sparsity averaging analysis algorithm that has been developed in the past and restores with high precision the intensity image from the visibilities. We also implement a similar framework that uses the CLEAN algorithm for image reconstruction, which helps compare the results between the two options and evaluate the quality of the measurements achieved.All our models are tested using a collection of simulated data that include objects of different profile types, ellipticity, size, orientation, and position in the field of view. The visibilities generation is done using either a simulated Gaussian profile coverage or a realistic SKA-like one. Additionally, we present an initial study of the same algorithms on real objects from the COSMOS survey.

EPFL2021

Iterative image subset scanning for image reconstruction from sensor signals

Giovanni Cherubini, Paul Hurley, Sanaz Kazemi, Matthieu Martin Jean-André Simeoni

Image reconstruction techniques from signals received by sensors find application in several fields, including radio interferometry for astronomical investigations and magnetic resonance imaging for medical applications. This paper presents a novel method for image reconstruction based on the iterative scanning of a region of interest. A modified approximate message passing (AMP) algorithm is adopted to extract relevant image information with low computational complexity from signals received by sensors. The method is illustrated by simulations, with reference to the LOFAR radio interferometer, and compared in the case of radio astronomy with the CLEAN algorithm.

2015

Imaging in radio interferometry by iterative subset scanning using a modified amp algorithm

Giovanni Cherubini, Paul Hurley, Sanaz Kazemi, Matthieu Martin Jean-André Simeoni

Imaging techniques in radio interferometry often face a significant challenge posed by the large number of antenna signals received, from which the image information needs to be extracted. Beam- forming is envisaged to reduce the rate required for transporting data from groups of antennas to a central site for further processing. We propose a novel method for image reconstruction based on the iterative scanning of a region of interest, combined with randomized beamforming. A modified approximate message-passing algorithm is adopted to extract relevant image information from beamformed signals received at the antenna stations. The method is illustrated by simulations, with reference to the LOFAR radio interferometer, and compared with the CLEAN algorithm.

2016

Development and evaluation of galaxy shape measurement algorithms for radio interferometric data

Vasileios Angelopoulos

EPFL2021