Galaxy Image Deconvolution for Weak Gravitational Lensing with Physics-informed Deep Learning

• URL: http://arxiv.org/abs/2211.01567v1
• Date: Thu, 3 Nov 2022 03:12:52 GMT
• Title: Galaxy Image Deconvolution for Weak Gravitational Lensing with Physics-informed Deep Learning
• Authors: Tianao Li and Emma Alexander
• Abstract summary: Removing optical and atmospheric blur from galaxy images significantly improves galaxy shape measurements. We introduce a so-called "physics-based deep learning" approach to the Point Spread (PSF) deconvolution problem in galaxy surveys.
• Score: 0.623075162128532
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Removing optical and atmospheric blur from galaxy images significantly improves galaxy shape measurements for weak gravitational lensing and galaxy evolution studies. This ill-posed linear inverse problem is usually solved with deconvolution algorithms enhanced by regularisation priors or deep learning. We introduce a so-called "physics-based deep learning" approach to the Point Spread Function (PSF) deconvolution problem in galaxy surveys. We apply algorithm unrolling and the Plug-and-Play technique to the Alternating Direction Method of Multipliers (ADMM) with a Poisson noise model and use a neural network to learn appropriate priors from simulated galaxy images. We characterise the time-performance trade-off of several methods for galaxies of differing brightness levels, showing an improvement of 26% (SNR=20)/48% (SNR=100) compared to standard methods and 14% (SNR=20) compared to modern methods.

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