2021-10-28, 10:00–10:15, Grand Ballroom
Radio telescopes spanning various locations across the Earth today are used as interferometer arrays to examine fine radio sky, providing scientists with a tremendous amount of data that can then be analysed to acquire objective information to understand the universe. Despite difficulties with large volumes of data coming from radio telescopes, radio interferometric reduction and imaging techniques show promising results in the current data-rich era. There is an excellent effort in the development of software in radio astronomy, and it is becoming available to the scientific community. Image fidelity is a measure of the accuracy of the reconstructed sky brightness distribution. A related metric, dynamic range, can also be used to measure the degree to which imaging artifacts around strong sources are suppressed, which implies a higher fidelity of the on-source reconstruction.
The main objective of this project is to develop a framework that allows for the evaluation of calibration, imaging and source finding techniques in astronomy. We quantify the ability of these techniques based on the recovery of source properties such as flux and astrometry. AIMfast (https://github.com/Athanaseus/aimfast) allows astronomers to analyse source catalogues by efficiently cross-matching these properties. Furthermore, global and localised image statistics (e.g. rms, mad, skewness etc.) can be computed using this tool. It is a Python-based package that can be executed from the command line and easily be integrated into a workflow environment. All visualisation outputs are saved in a portable Html format that can be loaded in any web browser. Moreover, a JSON file with all the statistics and source properties can be generated to use in other software programs.
Solutions for workflow management and reproducibility