发表机构
Department of Particle Physics and Astrophysics; Weizmann Institute of Science（粒子物理与天体物理系; 魏兹曼科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对天文成像调查中欠采样图像相加与相减方式不佳的问题，提出LUTRA方法，以最优方式执行相关过程，提升性能，便于测量且提供超分辨率结果，在公开数据上验证其性能比当前方法高1.25倍并开源实现。
AI 中文摘要
在天文成像调查中，为获取更深图像和探测新源会对同一天空区域进行重复观测，如寻找超新星、引力波光学对应体等瞬变现象时。许多此类调查中部分图像存在欠采样问题，即像素尺寸过大导致图像有混叠。欠采样图像的图像相加与背景减法方式欠佳，致使灵敏度降低和误报率增加。我们提出一种新方法（线性欠采样瞬变与相加法，LUTRA），以数学证明的最优方式执行这两个过程，能提升许多科学应用的性能，便于进行测光和天体测量等测量并提供超分辨率结果。我们在公开的ZTF数据上展示了该方法的性能，与当前方法相比信噪比提高了1.25倍，并提供了开源Python实现。
英文摘要
In astronomical imaging surveys, repeated observations of the same sky patches are taken in order to obtain deeper images and detect new sources. This is the case in the search for many transient phenomena, such as supernovae, gravitational wave (GW) optical counterparts and other cataclysmic variables. In many such surveys some of the images are undersampled, meaning that the pixel size is too large, and the image suffers from aliasing. For undersampled images, both co-addition of the images and background subtraction are done in a non-optimal manner, which leads to reduced sensitivity and an increased rate of false alarms. We present a new method (named Linear Undersampled Transients \& Addition (LUTRA)) that performs both processes in a mathematically proven optimal way, which allows improved performance for many scientific applications. It also allows easy and direct performance of measurements such as photometry and astrometry in a simple manner, while providing results in super-resolution. We demonstrate the performance of the method on public ZTF data and show $\times 1.25$ higher SNR compared to current methods. We provide an open source Python implementation.
