Imaging An Evolving Black Hole By Leveraging Shared Structure

High quality black hole videos can provide key evidence of astrophysical processes that single static images cannot provide. However, reconstructing a video of a black hole is a highly ill-posed problem, requiring additional structural constraints to produce a plausible solution. Traditional structural constraints on the spatial or temporal structure are subject to human bias. In our work, we adapt recently developed techniques to solve realistic black hole video reconstruction without direct priors on the spatial or temporal structure, mitigating human bias. In particular, we solve a set of per-frame imaging inverse problems by relying on the shared structure across different underlying frames of the black hole as regularization. We encode this shared structure through a deep generative neural network, requiring that the reconstructed frames all lie within the range of this shared generator. We demonstrate our framework on a set of synthetic measurements of a simulated video of the supermassive black hole M87*, showing that we can substantially outperform both traditional and modern imaging methods and even achieve a level of superresolution in the reconstructed frames.