Swarm intelligence for full Stokes dynamic imaging reconstruction of interferometric data

Abstract

Context. In very long baseline interferometry (VLBI), the combination of multiple antennas permits the synthesis of a virtual telescope with a larger diameter and consequently higher resolution than the individual antennas. However, due to the sparse nature of the array, recovering an image from the observed data is a challenging ill-posed inverse problem. Aims. The VLBI community is interested in not only recovering an image in total intensity from interferometric data, but also in obtaining results in the polarimetric and the temporal domain. Only a few algorithms are able to work in all these domains simultaneously. In particular, the algorithms based on optimization that consider various penalty terms specific to static total intensity imaging, time-variability and polarimetry are restricted to grids in the domain of the objective function. In this work we present a novel algorithm, multiobjective particle swarm optimization (MO-PSO), that is able to recover the optimal weights without any space-gridding, and to obtain the marginal contribution of each of the playing terms. Methods. To this end, we utilized multiobjective optimization together with particle swarm metaheuristics. We let the swarm of weights converge to the best position. Results. We evaluate our algorithm with synthetic data sets that are representative for the main science targets and instrumental configuration of the Event Horizon Telescope Collaboration (EHTC) and its planned successors. We successfully recover the polarimetric, static, and time-dynamic signature of the ground truth movie' even with relative sparsity, and a set of realistic data corruptions. Conclusions. We have built a novel, fast, hyperparameter space gridding-free algorithm that successfully recovers static and dynamic polarimetric reconstructions. Compared to regularized maximum likelihood (RML) methods, it avoids the need for parameter surveys, and it is not limited to the number of pixels, unlike recently proposed multiobjective imaging algorithms. Hence, this technique is a novel useful alternative tool to characterize full Stokes time-(in)dependent signatures in a VLBI data set robustly with a minimal set of user-based choices.