Image processing challenges in weak gravitational lensing

TL;DR

This paper reviews the challenges in image processing for weak gravitational lensing, emphasizing the need for advanced techniques from computer science and signal processing to handle upcoming large datasets and improve cosmological insights.

Contribution

It highlights the interdisciplinary opportunities for applying signal processing and machine learning to address specific challenges in weak lensing data analysis.

Findings

Current techniques may be insufficient for upcoming large datasets

Interdisciplinary approaches can improve lensing analysis

Key steps in the analysis chain face distinct processing challenges

Abstract

The field of weak gravitational lensing, which measures the basic properties of the Universe by studying the way that light from distant galaxies is perturbed as it travels towards us, is a very active field in astronomy. This short article presents a broad overview of the field, including some of the important questions that cosmologists are trying to address, such as understanding the nature of dark energy and dark matter. To do this, there is an increasing feeling within the weak lensing community that other disciplines, such as computer science, machine learning, signal processing and image processing, have the expertise that would bring enormous advantage if channelled into lensing studies. To illustrate this point, the article below outlines some of the key steps in a weak lensing analysis chain. The challenges are distinct at each step, but each could benefit from ideas developed in the signal processing domain. This article also gives a brief overview of current and planned lensing experiments that will soon bring about an influx of data sets that are substantially larger than those analysed to date. It is, therefore, inevitable that current techniques are likely to be insufficient, thus leading to an exciting era where new methods will become crucial for the continued success of the field.