Images of the Hidden Universe

Overflow Room: 201 A/B - Some of the most iconic images in modern science were never captured by a camera in the traditional sense. Instead, they were inferred from indirect and incomplete measurements, using a combination of physics, prior knowledge, and computation. In this talk, I will explore how physics and machine learning are working together to illuminate parts of the universe that are difficult - or even fundamentally impossible - to observe directly. I’ll begin with the story of black hole imaging, where theory long predicted what we should see, and where confidence came not from a single image, but from the consistency of features across many reconstructions of the same data. Along the way, I’ll show that this kind of inference is not unique to extreme astrophysics, but also underlies how we form images in familiar technologies we rely on every day, where images are similarly reconstructed from indirect measurements using models and assumptions. I’ll show that simple assumptions can take us far, but also where they begin to limit what we can learn. Incorporating richer assumptions through the help of machine learning allows us to extract more from the same data and explore a full range of possibilities that respects varying strengths of the expected physics. Finally, I will discuss how these ideas extend beyond black holes to other scientific imaging problems, including mapping the distribution of dark matter from subtle distortions in the shapes of galaxies due to gravitational lensing. Together, these examples illustrate how modern imaging increasingly relies on integrating physics and machine learning to extract meaningful information from fundamentally limited data to uncover our hidden universe.