A PyTorch benchmark for high-contrast imaging post processing

Chia-Lin Ko; Ewan S. Douglas; Justin Hom

doi:10.1117/12.3027407

3 October 2024 A PyTorch benchmark for high-contrast imaging post processing

Chia-Lin Ko, Ewan S. Douglas, Justin Hom

Proceedings Volume 13138, Applications of Machine Learning 2024; 1313811 (2024) https://doi.org/10.1117/12.3027407
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Conference Poster

Abstract

Direct imaging of exoplanets is a challenging task that involves distinguishing faint planetary signals from the overpowering glare of their host stars, often obscured by time-varying stellar noise known as ”speckles”. The predominant algorithms for speckle noise subtraction employ principal-based point spread function (PSF) fitting techniques to discern planetary signals from stellar speckle noise. We introduce torchKLIP, a benchmark package developed within the machine learning (ML) framework PyTorch. This work enables ML techniques to utilize extensive PSF libraries to enhance direct imaging post-processing. Such advancements promise to improve the post-processing of high-contrast images from leading-edge astronomical instruments like the James Webb Space Telescope and extreme adaptive optics systems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Chia-Lin Ko, Ewan S. Douglas, and Justin Hom "A PyTorch benchmark for high-contrast imaging post processing", Proc. SPIE 13138, Applications of Machine Learning 2024, 1313811 (3 October 2024); https://doi.org/10.1117/12.3027407

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