Combinatorial optimization on graphs for science operations in radio astronomy

Modern radio telescopes are often built as arrays of antennas that operate interferometrically to synthesize apertures much larger than that provided by a single element. Due to improvements in digitisation, these arrays are able to be reconfigured in a flexible manner for different scientific applications, including division into heterogeneous sub-arrays, bandwidth partitioning and antenna/baseline reweighting, often for multiple sub-arrays operating in parallel. Choosing an optimal set of configuration parameters for such partitions is a combinatorial optimization problem, subject to complexities that are often specific to every individual scientific use-case. In this project the student will explore the potential of modern methods in artificial intelligence, such as deep-learning, reinforcement learning and diffusion models, to learn the heuristics of array configuration for the science operations of the Square Kilometre Array Observatory (SKAO) and its associated facilities. In doing so, this work is intended to provide insight into how machine learning more generally can be employed to solve combinatorial optimization problems on graphs.

References

https://arxiv.org/pdf/1912.02175.pdf https://arxiv.org/abs/2302.08224 https://iopscience.iop.org/article/10.1086/422356/pdf

Additional information

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