Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors
This work addresses the challenge of image reconstruction in astronomy where direct observation is impossible, offering a method to evaluate biases from different priors, though it is incremental as it builds on existing Bayesian and generative modeling techniques.
The authors tackled the problem of reconstructing images of the black hole M87* from Event Horizon Telescope data by developing a framework that uses deep generative models as flexible priors to impose different image statistics, allowing assessment of how prior choices affect visual features and uncertainty in reconstructions for both simulated and real data.
Reconstructing images from the Event Horizon Telescope (EHT) observations of M87*, the supermassive black hole at the center of the galaxy M87, depends on a prior to impose desired image statistics. However, given the impossibility of directly observing black holes, there is no clear choice for a prior. We present a framework for flexibly designing a range of priors, each bringing different biases to the image reconstruction. These priors can be weak (e.g., impose only basic natural-image statistics) or strong (e.g., impose assumptions of black-hole structure). Our framework uses Bayesian inference with score-based priors, which are data-driven priors arising from a deep generative model that can learn complicated image distributions. Using our Bayesian imaging approach with sophisticated data-driven priors, we can assess how visual features and uncertainty of reconstructed images change depending on the prior. In addition to simulated data, we image the real EHT M87* data and discuss how recovered features are influenced by the choice of prior.