Co-addition and Subtraction of Undersampled Images
2026-07-20 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors address a problem in astronomical surveys where images with large pixels (undersampled images) cause blurry or poorly combined pictures. They introduce a new method called LUTRA that combines and cleans these images in a mathematically optimal way, improving how faint objects are detected. This method also makes it easier to measure positions and brightness of objects with better detail. They tested LUTRA on real data and found it increased signal quality by 25% compared to older techniques. The authors also shared their code openly for others to use.
astronomical imagingundersamplingaliasingimage co-additionbackground subtractionphotometryastrometrysuper-resolutionsignal-to-noise ratioZTF (Zwicky Transient Facility)
Authors
Matan Schlanger, Barak Zackay
Abstract
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.