Sparse Lens Inversion Technique (SLIT): lens and source separability from linear inversion of the source reconstruction problem

TL;DR

SLIT introduces a sparse regularisation-based linear inversion method for reconstructing and separating lensed sources and lens light in gravitational lensing, enabling flexible, high-resolution, and automated source reconstruction.

Contribution

The paper presents a novel sparse regularisation approach using wavelet bases for joint source and lens light reconstruction in gravitational lensing, without requiring adaptive meshes.

Findings

Effective separation of lens and source light in simulations

Reconstruction of complex luminous structures with high resolution

Automated regularisation parameter selection

Abstract

Strong gravitational lensing offers a wealth of astrophysical information on the background source it affects, provided the lensed source can be reconstructed as if it was seen in the absence of lensing. In the present work, we illustrate how sparse optimisation can address the problem. As a first step towards a full free-form lens modelling technique, we consider linear inversion of the lensed source under sparse regularisation and joint deblending from the lens light profile. The method is based on morphological component analysis, assuming a known mass model. We show with numerical experiments that representing the lens and source light using an undecimated wavelet basis allows us to reconstruct the source and to separate it from the foreground lens at the same time. Both the source and lens light have a non-analytic form, allowing for the flexibility needed in the inversion to represent arbitrarily small and complex luminous structures in the lens and source. in addition, sparse regularisation avoids over-fitting the data and does not require the use of any adaptive mesh or pixel grid. As a consequence, our reconstructed sources can be represented on a grid of very small pixels. Sparse regularisation in the wavelet domain also allows for automated computation of the regularisation parameter, thus minimising the impact of arbitrary choice of initial parameters. Our inversion technique for a fixed mass distribution can be incorporated in future lens modelling technique iterating over the lens mass parameters. The python package corresponding to the algorithms described in this article can be downloaded via the github platform at https://github.com/herjy/SLIT.