Frontier of Black Hole Imaging: Computational Algorithms Driving the Scientific Breakthroughs

In April 2019 and May 2021, hundreds of front pages worldwide featured the first images of the supermassive black holes M87* and Sgr A*, revealing the shadows cast by their event horizons—the visible edge of spacetime. These breakthroughs yielded the second most-cited ground-based astronomy result of the past decade and were delivered by the Event Horizon Telescope (EHT), a global network of (sub)millimeter radio telescopes. The EHT achieves the sharpest angular resolution of any existing astronomical instrument by computationally synthesizing an Earth-sized aperture using very long baseline interferometry, a technique of radio interferometry (RI).

As a computational telescope, this success relies centrally on innovative RI imaging algorithms tailored to the EHT. RI imaging is an underdetermined inverse problem: one must reconstruct an image from incomplete and noisy Fourier-space measurements. The EHT introduces two additional acute challenges—extremely sparse Fourier-plane coverage and severe data corruption arising from the scarcity of suitable calibrators at EHT resolution. The limitations of traditional methods motivated the development of dedicated techniques now known as regularized maximum likelihood (RML) methods, which couple the power of regularization with efficient data models robust to calibration errors. RML approaches have since been widely adopted across radio astronomy, well beyond the EHT.

RI algorithm development remains an active area of research, driven by the rapidly growing demand for larger-scale and more complex modeling of black holes enabled by planned extensions. Upcoming upgrades to the EHT promise major advances in temporal resolution, sensitivity, and frequency coverage, producing five-dimensional Fourier-space data across multiple spatial scales. Looking further ahead, community roadmaps envision extending the EHT into space with the proposed Black Hole Explorer (BHEX) mission, ushering in a new era of angular resolution. Rapid advances in artificial intelligence and its modern software infrastructure—particularly for accelerating Bayesian parameter inference and enabling more expressive and efficient regularization for high-dimensional images than traditional hand-crafted regularizers—have driven the development of AI-powered algorithms for EHT data processing and physical interpretation.

This talk will introduce radio interferometry through the lens of computational imaging, present the algorithms underpinning black hole imaging, and outline the emerging AI-enabled frontier designed to meet the demands of next-generation facilities. I will also highlight cross-disciplinary relevance to other inverse problems in the physical sciences, including medical imaging.