Reconstructing Video of Time-Varying Sources From Radio Interferometric Measurements

Abstract

Very long baseline interferometry (VLBI) makes it possible to recover images of astronomical sources with extremely high angular resolution. Most recently, the Event Horizon Telescope (EHT) has extended VLBI to short millimeter wavelengths with a goal of achieving angular resolution sufficient for imaging the event horizons of nearby supermassive black holes. VLBI provides measurements related to the underlying source image through a sparse set spatial frequencies. An image can then be recovered from these measurements by making assumptions about the underlying image. One of the most important assumptions made by conventional imaging methods is that over the course of a night's observation the image is static. However, for quickly evolving sources, such as the galactic center's supermassive black hole (SgrA*) targeted by the EHT, this assumption is violated and these conventional imaging approaches fail. In this work we propose a new way to model VLBI measurements that allows us to recover both the appearance and dynamics of an evolving source by reconstructing a video rather than a static image. By modeling VLBI measurements using a Gaussian Markov Model, we are able to propagate information across observations in time to reconstruct a video, while simultaneously learning about the dynamics of the source's emission region. We demonstrate our proposed Expectation-Maximization (EM) algorithm, StarWarps, on realistic synthetic observations of black holes, and show how it substantially improves results compared to conventional imaging algorithms. Additionally, we demonstrate StarWarps on real VLBI data of the M87 Jet from the VLBA.

First M87 Event Horizon Telescope Results. I. The Shadow of the Supermassive Black Hole

“…Another primary EHT source, Sgr A * , has a precisely measured mass three orders of magnitude smaller than that of M87 * , with dynamical timescales of minutes instead of days. Observing the shadow of Sgr A * will require accounting for this variability and mitigation of scattering effects caused by the interstellar medium (Johnson 2016;Lu et al 2016;Bouman et al 2018). Time dependent nonimaging analysis can be used to potentially track the motion of emitting matter near the black hole, as reported recently through interferometric observations in the near-infrared (Gravity Collaboration et al 2018b).…”

Metrics and Motivations for Earth–Space VLBI: Time-resolving Sgr A* with the Event Horizon Telescope

“…The combination of the EHT array and VLBI imaging algorithms designed to address the EHT's particular challenges is expected to be capable of reconstructing static images of Sgr A* at this resolution, and has done so for M87 (see, e.g., Honma et al 2014;Bouman et al 2016;Chael et al 2016;Johnson et al 2017;Akiyama et al 2017a,b;Bouman et al 2018;Kuramochi et al 2018;Chael et al 2018a;Event Horizon Telescope Collaboration et al 2019b). However, imaging time-variable structure around supermassive black holes requires well-sampled spatial baseline coverage (conventionally described in the (u, v) plane) on timescales comparable to the innermost stable circular orbit (or ISCO).…”

A deeper look into natural sciences with physics-based and data-driven measures

“…The capillaries of the glymphatic system are not directly visible either in the raw video data, see Figure 3 A, or in the mean obtained by using a commercial deep learning denoising autoencoder from the “Deep Learning Toolbox” of MATLAB ( Zhang et al, 2017 ; Berghout et al., 2020 , Berghout, 2021 ), see Figure 3 B. Application of the common GMM entropy ( Zoran and Weiss, 2011 ; Bouman et al., 2018 ; The Event Horizon Telescope Collaboration, 2019a ; Greggio et al., 2012 ) does allow to visualize only surface capillaries, see Figure 3 C. The deeper lying bulk capillaries are not directly visible and can only be revealed through latent effects. As can be seen from the Figure 3 D, applying the latent entropy measure one can extract and visualize the capillary pattern, including the bulk ones.…”

“…Computational and statistical methods to identify subtle features in the noisy video data are of large importance for astronomical observations and allowed for important discoveries in the past, such as the cosmic microwave background radiation, gravitational waves, existence and motion of exoplanets, and imaging of black holes ( Zoran and Weiss, 2011 ; Greggio et al., 2012 ; Bouman et al., 2018 ; The Event Horizon Telescope Collaboration, 2019b ). The high accuracy and the low computational cost of the latent measures may potentially help to enhance a detection of weak latent features in these application domains.…”