A PyTorch Benchmark for High-Contrast Imaging Post Processing

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

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arXiv · 2409.16466

A PyTorch Benchmark for High-Contrast Imaging Post Processing

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

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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.

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Chia-Lin Ko, Ewan S. Douglas, Justin Hom. 2024-09-24. A PyTorch Benchmark for High-Contrast Imaging Post Processing. https://arxiv.org/abs/2409.16466

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