Image Coaddition with Temporally Varying Kernels

TL;DR

This paper evaluates an online Fourier domain method for combining large, multi-frequency astronomical images into a single template, addressing computational challenges in real-time large-scale surveys.

Contribution

It provides a comparative analysis of a proposed online Fourier domain image coaddition method against simpler approaches, highlighting its performance benefits in large-scale survey data processing.

Findings

The online Fourier method improves image quality over simpler methods.

Simulation results demonstrate the method's effectiveness in large datasets.

Additional complexity yields significant performance gains.

Abstract

Large, multi-frequency imaging surveys, such as the Large Synaptic Survey Telescope (LSST), need to do near-real time analysis of very large datasets. This raises a host of statistical and computational problems where standard methods do not work. In this paper, we study a proposed method for combining stacks of images into a single summary image, sometimes referred to as a template. This task is commonly referred to as image coaddition. In part, we focus on a method proposed in previous work, which outlines a procedure for combining stacks of images in an online fashion in the Fourier domain. We evaluate this method by comparing it to two straightforward methods through the use of various criteria and simulations. Note that the goal is not to propose these comparison methods for use in their own right, but to ensure that additional complexity also provides substantially improved performance.