Using multiobjective optimization to reconstruct interferometric data (I)

Abstract

Imaging in radioastronomy is an ill-posed inverse problem. Particularly the Event Horizon Telescope (EHT) Collaboration investigated the fidelity of their image reconstructions convincingly by large surveys solving the problem with different optimization parameters. This strategy faces a limitation for the existing methods when imaging the active galactic nuclei (AGN): large and expensive surveys solving the problem with different optimization parameters are time-consumptive. We present a novel nonconvex, multiobjective optimization modeling approach that gives a different type of claim and may provide a pathway to overcome this limitation. 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).

arXiv commentto appear in A&A

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• Imaging a ring-like structure and the extended jet of M87 at 86 GHz
• Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors
• Swarm intelligence for full Stokes dynamic imaging reconstruction of interferometric data
• Deep learning inference with the Event Horizon Telescope II. The Zingularity framework for Bayesian artificial neural networks
• Prospects of using closure traces directly for imaging in Very Long Baseline Interferometry
• How to make CLEAN variants faster? Using clustered components informed by the autocorrelation function
• Probing circular polarization and magnetic field structure in AGN
• Accelerating the CLEAN algorithm of radio interferometry with convex optimization
• Very-Long Baseline Interferometry Imaging with Closure Invariants using Conditional Image Diffusion
• Multiobjective optimization for scattering mitigation and scattering screen reconstruction in VLBI observations of the Galactic Center