Work in collaboration with Sydney students
Alison Wong (PhD)
and Louis Desdoigts (Honours),
Yinzi Xin (Caltech),
and faculty Peter Tuthill (Sydney)
and Laurent Pueyo (STScI).
The main limitation on direct imaging is from wavefront aberrations which corrupt phase information.
Given an image, what were the aberrations in the telescope?
Given an objective, how do we engineer an optimal PSF?
Phase Apodized Coronagraph: Por, 2019, arXiv:1908.02585
How do we correct phase errors in postprocessing?
See our paper: arXiv:2011.09780!
What if we want to linearize an arbitrary optical system?
Optics is mathematically like machine learning: matrix multiplications and simple nonlinear functions
Can use automatic differentiation!
Autodiff is not finite differences, and it is not symbolic differentiation.
Using the chain rule you can decompose almost-arbitrary code!
Autodiff is the enabling technology for deep neural networks - you use the chain rule to take derivatives of nearly-arbitrary numerical functions.
Implementations in TensorFlow, Theano, PyTorch, Julia...
Here we use Google Jax, which resembles NumPy, to rewrite the Fourier/Fresnel optics code poppy to take derivatives
Alison Wong - phase retrieval and design by gradient descent
Coronagraph Phase Mask Design - try it yourself!
Detect planets with μ-arcsec astrometry
Astrometric precision proportional to gradient energy
Use diffractive optic to maximize this subject to constraints
Louis Desdoigts - sensitivity of Toliman to Zernike modes
Basis used in CLIMB
What else can we use this for?