DUE TO LIMITED AUDIENCE CAPACITY in WEGMANS HALL, WE RECORDED THIS TALK WHICH IS AVAILABLE AT THE FOLLOWING LINK.
A technical sound issue happed in minute 40:00 of the recording and resolved at minute 46:00. An improved captioned version will be uploaded in a few days.
The Goergen Institute for Data Science welcomes distinguished speaker Katie Bouman. This seminar is supported by the National Science Foundation Research Traineeship Data Enabled Science and Engineering (NRT-DESE) Award.
Abstract: This talk will present the methods and procedures used to produce the first image of a black hole from the Event Horizon Telescope, as well as future developments. It had been theorized for decades that a black hole would leave a "shadow" on a background of hot gas. Taking a picture of this black hole shadow would help to address a number of important scientific questions, both on the nature of black holes and the validity of general relativity. Unfortunately, due to its small size, traditional imaging approaches require an Earth-sized radio telescope. In this talk, I discuss techniques the Event Horizon Telescope Collaboration has developed to photograph a black hole using the Event Horizon Telescope, a network of telescopes scattered across the globe. Imaging a black hole’s structure with this computational telescope required us to reconstruct images from sparse measurements, heavily corrupted by atmospheric error. This talk will summarize how the data from the 2017 observations were calibrated and imaged, and explain some of the challenges that arise with a heterogeneous telescope array like the EHT. The talk will also discuss how we are developing machine learning methods to help design future telescope arrays.
Bio: Katie Bouman is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. Before joining Caltech, she was a postdoctoral fellow in the Harvard-Smithsonian Center for Astrophysics. She received her Ph.D. in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT in EECS. Before coming to MIT, she received her bachelor's degree in Electrical Engineering from the University of Michigan. The focus of her research is on using emerging computational methods to push the boundaries of interdisciplinary imaging.
For more information on Katie's work, please see the following links:
Friday, January 17 at 3:00pm
Wegmans Hall, Auditorium 1400
250 Hutchison Rd, Rochester, NY 14620