标题： dfreproject：用于天文投影转换的Python包 显示英文标题

标题： dfreproject: A Python package for astronomical reprojection

摘要： 深度天文图像是通过将许多单独的子曝光数字化叠加起来构建的。 由于天体的运动、光学机械成像链的变化/缺陷以及其他因素，每种子曝光预计会显示出星体和其他物体在场中的位置存在微小差异。 为了最大化图像质量，必须确保在叠加之前，每种子曝光都对齐到一个共同的参考框架。 这是通过将每个曝光重新投影到一个使用世界坐标系统（WCS）定义的公共目标网格上来实现的，该系统通过将参考对象已知的角度位置映射到每张图像上观察到的空间位置来定义。 用于重新投影图像所需的变换涉及复杂的三角表达式，这些表达式计算起来可能很慢，因此重新投影可能是图像处理管道中的主要瓶颈。 为了使天文重新投影在管道中更快地实现，我们编写了`dfreproject`，这是一个用于此目的的GPU优化函数的Python包。 该包的功能通过使用 正形投影来分解坐标变换，以定义从源平面到目标平面逐像素的位移。 该包还提供了工具，通过单个函数调用将源图像插值到目标平面上。 该模块遵循在经典论文中概述的FITS和SIP格式。 与常见的替代方法相比，当在GPU上运行时，`dfreproject`的例程可以快达20倍，而在CPU上运行时则快达10倍。 显示英文摘要

摘要： Deep astronomical images are often constructed by digitially stacking many individual sub-exposures. Each sub-exposure is expected to show small differences in the positions of stars and other objects in the field, due to the movement of the celestial bodies, changes/imperfections in the opto-mechanical imaging train, and other factors. To maximize image quality, one must ensure that each sub-exposure is aligned to a common frame of reference prior to stacking. This is done by reprojecting each exposure onto a common target grid defined using a World Coordinate System (WCS) that is defined by mapping the known angular positions of reference objects to their observed spatial positions on each image. The transformations needed to reproject images involve complicated trigonometric expressions which can be slow to compute, so reprojection can be a major bottleneck in image processing pipelines. To make astronomical reprojections faster to implement in pipelines, we have written `dfreproject`, a Python package of GPU-optimized functions for this purpose. The package's functions break down coordinate transformations using Gnomonic projections to define pixel-by-pixel shifts from the source to the target plane. The package also provides tools for interpolating a source image onto a target plane with a single function call. This module follows the FITS and SIP formats laid out in the seminal papers. Compared to common alternatives, `dfreproject`'s routines result in speedups of up to 20X when run on a GPU and 10X when run on a CPU.