Astronomy Lecture - Images of the Hidden Universe

Name: Astronomy Lecture - Images of the Hidden Universe
Start: 2026-05-29T20:00:00-07:00
Location: 1216 E California Blvd, Pasadena, CA, United States, California 91125

Schedule

Fri, 29 May, 2026 at 08:00 pm

UTC-07:00

Location

1216 E California Blvd, Pasadena, CA, United States, California 91125 | Pasadena, CA

How do scientists use AI, physics, and telescope observations to "photograph" black holes, dark matter, and other exotic astrophysical phenomena? Join us for a lecture by Professor Katie Bouman, member of the Event Horizon Telescope that took the first picture of a black hole, as she discusses these novel analytic techniques. Her presentation will be followed by a panel Q&A consisting of several astrophysicists to answer your questions about astronomy and space science.
This public astronomy event will be hosted both in-person as well as live-streamed over YouTube Live. The lecture will be 30 minutes, followed by a 90-minute session of telescope-aided stargazing and a Q&A Panel consisting of experts in the department on a variety of astronomy and astrophysical topics. You can attend in person or interact with us through the YouTube interface. Event is free and open to all, no reservations necessary.
For more information including a link to the YouTube Livestream and directions to Caltech, click on the poster image or visit our webpage: http://outreach.astro.caltech.edu
Title: Images of the Hidden Universe
Lecturer: Katie Bouman
Abstract:
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.