New-generation Maximum Entropy Method (ngMEM): a Lagrangian-based algorithm for dynamic reconstruction of interferometric data

Abstract

Imaging interferometric data in radio astronomy requires the use of non-linear algorithms that rely on different assumptions on the source structure and may produce non-unique results. This is especially true for Very Long Baseline Interferometry (VLBI) observations, where the sampling of Fourier space is very sparse. A basic tenet in standard VLBI imaging techniques is to assume that the observed source structure does not evolve during the observation. However, the recent VLBI results of the supermassive black hole (SMBH) at our Galactic Center (Sagittarius A$^*$, SgrA*), recently reported by the Event Horizon Telescope Collaboration (EHTC), require the development of dynamic imaging algorithms, since it exhibits variability at minute timescales. In this paper, we introduce a new non-convex optimization problem that extends the standard Maximum Entropy Method (MEM), for reconstructing intra-observation dynamical images from interferometric data that evolves in every integration time. We present a rigorous mathematical formalism to solve the problem via the primal-dual approach. We build a Newton strategy and we give its numerical complexity. We also give a strategy to iteratively improve the obtained solution and finally, we define a novel figure of merit to evaluate the quality of the recovered solution. Then, we test the algorithm, called ngMEM, in different synthetic datasets, with increasing difficulty. Finally, we compare it with another well-established dynamical imaging method. Within this comparison we identified a significant improvement of the ngMEM reconstructions. Moreover, the evaluation of the integration time evolution scheme and the time contribution showed to play a crucial role for obtaining good dynamic reconstructions.