http://arxiv.org/abs/2304.12107
Imaging in radioastronomy is an ill-posed inverse problem. Particularly the Event Horizon Telescope (EHT) Collaboration faces two big limitations for the existing methods when imaging the active galactic nuclei (AGN): large and expensive surveys solving the problem with different optimization parameters must be done, and only one local minima for each instance is returned. With our novel nonconvex, multiobjective optimization modeling approach, we aim to overcome these limitations. To this end we used a multiobjective version of the genetic algorithm (GA): the Multiobjective Evolutionary Algorithm Based on Decomposition, or MOEA/D. GA strategies explore the objective function by evolutionary operations to find the different local minima, and to avoid getting trapped in saddle points. First, we have tested our algorithm (MOEA/D) using synthetic data based on the 2017 Event Horizon Telescope (EHT) array and a possible EHT + next-generation EHT (ngEHT) configuration. We successfully recover a fully evolved Pareto front of non-dominated solutions for these examples. The Pareto front divides into clusters of image morphologies representing the full set of locally optimal solutions. We discuss approaches to find the most natural guess among these solutions and demonstrate its performance on synthetic data. Finally, we apply MOEA/D to observations of the black hole shadow in Messier 87 (M87) with the EHT data in 2017. MOEA/D is very flexible, faster than any other Bayesian method and explores more solutions than Regularized Maximum Likelihood methods (RML). We have done two papers to present this new algorithm: the first explains the basic idea behind multi-objective optimization and MOEA/D and it is used to recover static images, while in the second paper we extend the algorithm to allow dynamic and (static and dynamic) polarimetric reconstructions.
H. Müller, A. Mus and A. Lobanov
Tue, 25 Apr 23
31/72
Comments: accepted for publication in A&A, both first authors have contributed equally to this work
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