Imaging and Design with Differentiable Physics Models

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• Benjamin Pope (Macquarie University)
• Monday 02 June 2025, 16:00-17:00
• Martin Ryle Seminar Room, KICC.

If you have a question about this talk, please contact km723.

The technology that underpins machine learning – differentiable programming – is poised to revolutionise astronomy, making it possible for the first time to fit very high dimensional models: hierarchical models describing many objects; the sensitivity of millions of pixels in a detector; models of images or spectra with very many free parameters; or neural networks that represent physics we cannot easily solve in closed form. It also enables fundamental information-theoretic quantities like the Fisher information to be calculated, allowing for determination and optimization of the information content of an experiment. I will discuss how we apply 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 related work on interferometry, transits and AGN reverberation mapping in our group.

This talk is part of the Astro Data Science Discussion Group series.

This talk is included in these lists:

• Astro Data Science Discussion Group
• Cambridge Astronomy Talks
• Combined External Astrophysics Talks DAMTP
• Cosmology, Astrophysics and General Relativity
• Institute of Astronomy Extra Talks
• Institute of Astronomy Talk Lists
• Martin Ryle Seminar Room, KICC

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