Learning to See Sharper: Deep Learning for Astronomical Data Enhancement

Speaker

Shoubaneh Hemmati (Caltech/IPAC)

Abstract

Astronomical surveys inherently trade off coverage, depth, and resolution, presenting an ill-posed inverse problem where classical deconvolution methods plateau. By leveraging survey overlaps as empirical priors, deep learning models learn direct low- to high-resolution mappings to enhance wide-area archives. I will present specialized neural architectures tailored to distinct imaging and spectroscopic challenges, from generative models that deblend crowded fields and super-resolve infrared observations, to shape-preserving residual networks for weak lensing, diffusion models for realistic survey simulations, and physics-informed models that resolve blended spectral emission lines. Matching right-sized architectures to specific scientific measurements shows how learned priors can scale data enhancement across next-generation surveys.

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