20260530 < Main

---+ May 29, 2026 8-10 PM
*Event Type*: [[PublicLectures][Lecture and Stargazing]]<br>
*Title*: Images of the Hidden Universe<br>
*Lecturer*: Katie Bouman<br>
*Position*: Professor<br>
*Institution*: Caltech<br>
*Abstract*: <br>
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 how our Event Horizon Telescope team created the first direct image of a black hole.  Theory had long predicted what we should see, and confidence came not from a single image, but from the consistency of features across many reconstructions of the same data.  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.
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<img src="%ATTACHURLPATH%/20260529V.jpg" alt="20260529V.jpg" width="768" height="1024" />

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