Optimal SKA Dish Configuration using Genetic Algorithms
Adam Gauci, Kristian Zarb Adami, John Abela, Babak E. Cohanim

TL;DR
This paper applies genetic algorithms to optimize the configuration of dishes and antennas in the SKA radio telescope, aiming to improve resolution and coverage through innovative layout solutions.
Contribution
It introduces a genetic algorithm-based approach to determine optimal SKA dish configurations, considering multiple fitness functions for layout quality.
Findings
Genetic algorithms effectively explore large configuration spaces.
Multiple fitness functions help identify high-quality dish layouts.
Optimized configurations improve UV coverage and reduce wire length.
Abstract
The Square Kilometre Array (SKA) is a radio telescope designed to operate between 70MHz and 10GHz. Due to this large bandwidth, the SKA will be built out of different collectors, namely antennas and dishes to cover the frequency range adequately. In order to deal with this bandwidth, innovative feeds and detectors must be designed and introduced in the initial phases of development. Moreover, the required level of resolution may only be achieved through a groundbreaking configuration of dishes and antennas. Due to the large collecting area and the specifications required for the SKA to deliver the promised science, the configuration of the dishes and the antennas within stations is an important question. This research builds on the work done before by Cohanim et al. (2004), Hassan et al. (2005) and Grigorescu et al. (2009) to further investigate the applicability of machine learning…
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Taxonomy
TopicsRadio Astronomy Observations and Technology · Antenna Design and Optimization
