提出新方法提升天文图像欠采样下的叠加与背景消除效果
Co-addition and Subtraction of Undersampled Images
- 基于数学最优原理，统一处理欠采样图像的叠加与背景减法
- 在ZTF数据上实现1.25倍信噪比提升，检测灵敏度显著增强
- 支持超分辨率测量，适合瞬变源搜索与光度/天体测量应用
在天文成像巡天中，对同一天空区域重复观测以获取更深图像并发现新源，尤其适用于超新星、引力波光学对应体等瞬变现象的探测。许多巡天中部分图像存在欠采样问题（像素过大导致混叠），现有方法在图像叠加和背景减除上非最优，降低探测灵敏度并增加误报率。本文提出新方法LUTRA，从数学上证明其在欠采样图像叠加与背景减除中的最优性，可直接实现超分辨率下的光度与天体测量。在公开的ZTF数据上验证，相比现有方法信噪比提升1.25倍。提供开源Python实现。
原文摘要 · Abstract (English)
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.
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