@inproceedings{1402b41aaefc4cd0a70f504fd2ea24a1,
title = "A PyTorch Benchmark for High-Contrast Imaging Post Processing",
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.",
keywords = "Exoplanets, High-Contrast Imaging, Machine Learning",
author = "Ko, {Chia Lin} and Douglas, {Ewan S.} and Justin Hom",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Applications of Machine Learning 2024 ; Conference date: 20-08-2024 Through 22-08-2024",
year = "2024",
doi = "10.1117/12.3027407",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Zelinski, {Michael E.} and Taha, {Tarek M.} and Barath Narayanan and Awwal, {Abdul A.} and Iftekharuddin, {Khan M.}",
booktitle = "Applications of Machine Learning 2024",
}