Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors

TL;DR

This paper introduces a flexible Bayesian framework using deep generative models as priors for reconstructing images of M87* from EHT data, allowing analysis of how different assumptions affect the reconstructed features.

Contribution

The paper presents a novel Bayesian imaging approach employing score-based deep generative priors to assess the impact of various priors on black hole image reconstructions.

Findings

Different priors influence the visual features of reconstructed images.

The framework can quantify uncertainty in the reconstructions.

Application to real EHT data demonstrates prior-dependent features.

Abstract

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.