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

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.

References in corpus (15)

• First M87 Event Horizon Telescope Results. I. The Shadow of the Supermassive Black Hole
• First M87 Event Horizon Telescope Results. VI. The Shadow and Mass of the Central Black Hole
• First M87 Event Horizon Telescope Results. IV. Imaging the Central Supermassive Black Hole
• First M87 Event Horizon Telescope Results. V. Physical Origin of the Asymmetric Ring
• First M87 Event Horizon Telescope Results. II. Array and Instrumentation
• First M87 Event Horizon Telescope Results. III. Data Processing and Calibration
• First Sagittarius A* Event Horizon Telescope Results. III: Imaging of the Galactic Center Supermassive Black Hole
• Imaging the Schwarzschild-radius-scale Structure of M87 with the Event Horizon Telescope using Sparse Modeling
• Imaging the Supermassive Black Hole Shadow and Jet Base of M87 with the Event Horizon Telescope
• The Image of the M87 Black Hole Reconstructed with PRIMO
• The Accretion flow in M87 is really MAD
• How narrow is the M87* ring? II. A new geometric model
• Using multiobjective optimization to reconstruct interferometric data (I)
• Multi-scale and Multi-directional VLBI Imaging with CLEAN
• alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction

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• Deep learning inference with the Event Horizon Telescope II. The Zingularity framework for Bayesian artificial neural networks
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• Very-Long Baseline Interferometry Imaging with Closure Invariants using Conditional Image Diffusion