标题： 一种用于高对比度成像后处理的PyTorch基准 显示英文标题

标题： A PyTorch Benchmark for High-Contrast Imaging Post Processing

摘要： 直接成像外行星是一项具有挑战性的任务，涉及从其宿主恒星的强烈光芒中区分微弱的行星信号，这些信号常常被随时间变化的恒星噪声（称为“斑点”）所掩盖。 消除斑点噪声的主要算法采用基于主成分的点扩散函数（PSF）拟合技术，以区分行星信号和恒星斑点噪声。 我们引入了torchKLIP，这是一个在机器学习（ML）框架PyTorch中开发的基准包。 这项工作使ML技术能够利用广泛的PSF库来增强直接成像后处理。 这些进步有望提高来自领先天文仪器（如詹姆斯·韦布空间望远镜和极端自适应光学系统）的高对比度图像的后处理。 显示英文摘要

摘要： 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.