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Deep learning in high angular-resolution radio interferometry

PhD ceremony:S. (Samira) Rezaei Badafshani
When:June 27, 2022
Supervisors:J.P. (John) McKean, Prof Dr, M. (Michael) Biehl, Prof Dr
Where:Academy building RUG
Faculty:Science and Engineering
Deep learning in high angular-resolution radio interferometry

This thesis has addressed several challenges of the big data era in the field of high angular-resolution radio astronomy using machine learning algorithms. The methodologies presented in this thesis were designed with the aim of minimizing the need for human interactions, while still providing robust results. This thesis has an interdisciplinary approach and uses knowledge in computer science to advance our understanding of the radio sky. The main objectives of this thesis can be categorized into four subjects. First, it provides an analysis to the properties of the detected radio sources with Very Long Baseline Array (VLBA). Then we have provided the details of our developed source detection and characterization pipeline that can localize the source in any observed image from the VLBA. Beside source detection, the implemented pipeline can remove the observational noise, restore the structure of the celestial sources and predict their properties, such as size and brightness. In the fourth chapter, we have designed an algorithm that can find rare types of galaxies, called strong gravitationally lensed systems, among the many observed radio emitting objects observed with the International LOFAR Telescope. We also have provided preliminary results on using machine learning algorithms to predict the lensing parameters such as the Einstein radius, axis ratio and position angle.