A short review on joint weak and strong cluster lens-mass reconstruction

TL;DR

This review discusses the integration of weak and strong gravitational lensing data for cluster mass reconstruction, highlighting recent methods, challenges, and future improvements including machine learning and additional lensing data types.

Contribution

It provides a comprehensive overview of inverse methods combining weak and strong lensing, emphasizing the potential of machine learning and data enhancements for improved cluster mass mapping.

Findings

Inverse methods successfully merge weak and strong lensing data.

Flexion measurements improve sub-structure detection.

Automation with machine learning is a promising future direction.

Abstract

The distinction between weak and strong lensing is somewhat arbitrary, and both regimes are manifestations of the same physical phenomenon: gravity bending the path of light. Nevertheless, these two regimes have to a large extent been treated separately, since they require different approaches. This review traces the development of methods combining weak-lensing and strong-lensing data for joint lens-mass reconstruction, with a particular emphasis on cluster lenses, where both effects occur. We conclude that so-called inverse methods have been successful in merging the two regimes insofar data analysis is concerned. However, a number of improvements seem to be in place. First, not many studies include weak lensing data beyond shear. In light of the unprecedented quality of the data of JWST and future surveys, this is a clear point of improvement. Especially so since flexion terms have proven useful in determining sub-structures. Second, considering the amount of data available, and the complexity of non-parametric lenses, automating the processes of lens-mass reconstruction would be beneficial. Towards this end, invoking machine learning seems like a promising way forward.