使用查找表对欠采样PSF进行定中
Centroiding Undersampled PSFs with a Lookup Table
AI总结：
针对无法抖动观测时的欠采样PSF定中问题，提出通过模拟PSF构建查找表，以最小平方残差匹配观测PSF，在噪声下比拟合算法提供更优的定中精度。
摘要：
我们提出了一种对欠采样点扩散函数（PSF）进行定中的方法，该方法可能非常有用，特别是在无法使用抖动观测的情况下。如果通过观测所用望远镜和探测器的特性校准，能够较好地了解预期的PSF轮廓，就可以在模拟的像素网格上的多个位置模拟欠采样PSF。由于PSF是模拟的，其真实定中位置是已知的，因此可以将每个欠采样PSF图像与其真实定中位置相匹配，从而形成一个查找表。然后，对于观测到的PSF，将其定中位置分配给查找表中与该观测PSF具有最小平方残差的PSF相关联的位置。我们考察了几种PSF尺寸，并证明当PSF欠采样时，与拟合算法相比，即使在存在噪声的情况下，查找表也能提供更好的定中位置。
英文摘要：
We present a method of centroiding undersampled point spread functions (PSFs) that may be useful, especially when dithering is not an option. If the profile of the expected PSF is known fairly well through characterization of the telescope and detector used for observing, one can simulate the undersampled PSF at many positions on a simulated pixel grid. The true centroid positions are known since the PSFs are simulated, and so one can match up each undersampled PSF images to its true centroid location, thus forming a lookup table. One then assigns the centroid position of an observed PSF to the position associated with the PSF in the lookup table that has the smallest squared residual with respect to the observed PSF. We examine a few PSF sizes and demonstrate that the lookup table provides better centroid positions compared to a fitting algorithm when the PSFs are undersampled, even in the presence of noise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
