Computational Imaging for Scientific Discovery: From Cloud Physics to Black Holes Dynamics
Imaging plays a key role in advancing science, from revealing the internal structure of clouds to the first visual evidence of a black hole. While both examples come from different imaging systems, they illustrate what can be achieved with modern computational approaches. Computational imaging combines concepts from physics, machine learning, and signal processing to reveal hidden structures at the smallest and largest of scales. In this talk, I will highlight how peeling away layers of the underlying physics leads to a spectrum of algorithms targeting new scientific discoveries. I will focus on the Event Horizon Telescope (EHT); a unique computational camera with the goal of imaging the glowing fluid surrounding supermassive black holes. In May of 2022, the EHT collaboration revealed the first images of the black hole at the center of our galaxy: Sagittarius A* (Sgr A*). These images were computationally reconstructed from measurements taken by synchronized telescopes around the globe. While images certainly offer interesting insights, looking toward the future, we are developing new computational algorithms that aim to go beyond a 2D image. For example, could we use EHT observations to recover the dynamic evolution or even the 3D structure? We tackle these challenges by integrating emerging AI concepts with physics models. Our hope is that in the not-too-distant future, these new and exciting prospects will enable scientific discovery and even provide a glimpse into the very nature of space-time itself in our galaxy's most extreme environment.