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SUMMARY:Imaging and Design with Differentiable Physics Models - Benjamin P
 ope (Macquarie University)
DTSTART:20250602T150000Z
DTEND:20250602T160000Z
UID:TALK230014@talks.cam.ac.uk
CONTACT:65128
DESCRIPTION:The technology that underpins machine learning - differentiabl
 e programming - is poised to revolutionise astronomy\, making it possible 
 for the first time to fit very high dimensional models: hierarchical model
 s describing many objects\; the sensitivity of millions of pixels in a det
 ector\; models of images or spectra with very many free parameters\; or ne
 ural networks that represent physics we cannot easily solve in closed form
 . It also enables fundamental information-theoretic quantities like the Fi
 sher information to be calculated\, allowing for determination and optimiz
 ation of the information content of an experiment. I will discuss how we a
 pply this to the James Webb interferometer experiment\, to provide a data-
 driven self-calibration of the telescope's highest resolution mode and its
  difficult systematics\; to design the Toliman Space Telescope to do high-
 precision\, distortion-tolerant astrometry\; and give an overview of relat
 ed work on interferometry\, transits and AGN reverberation mapping in our 
 group.\n
LOCATION:Martin Ryle Seminar Room\, KICC
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