SLITronomy: towards a fully wavelet-based strong lensing inversion technique

TL;DR

SLITronomy introduces a wavelet-based inversion method for strong gravitational lensing that improves source reconstruction accuracy and computational efficiency, aiding automated modeling and cosmological measurements.

Contribution

It presents an enhanced sparsity-based technique using wavelets for lens inversion, integrated into Lenstronomy, and validated on simulated and real data.

Findings

Wavelet representation captures detailed substructures in lensed sources.

Method outperforms shapelet-based approaches in quality and speed.

Applicable to current and future high-resolution telescope data.

Abstract

Strong gravitational lensing provides a wealth of astrophysical information on the baryonic and dark matter content of galaxies. It also serves as a valuable cosmological probe by allowing us to measure the Hubble constant independently of other methods. These applications all require the difficult task of inverting the lens equation and simultaneously reconstructing the mass profile of the lens along with the original light profile of the unlensed source. As there is no reason for either the lens or the source to be simple, we need methods that both invert the lens equation with a large number of degrees of freedom and also enforce a well-controlled regularisation that avoids the appearance of spurious structures. This can be beautifully accomplished by representing signals in wavelet space. Building on the Sparse Lens Inversion Technique (SLIT), in this work we present an improved sparsity-based method that describes lensed sources using wavelets and optimises over the parameters given an analytical lens mass profile. We apply our technique on simulated HST and E-ELT data, as well as on real HST images of lenses from the Sloan Lens ACS (SLACS) sample, assuming a lens model. We show that wavelets allow us to reconstruct lensed sources containing detailed substructures when using both present-day data and high-resolution images from future thirty-meter-class telescopes. Wavelets moreover provide a much more tractable solution in terms of quality and computation time compared to using a source model that combines smooth analytical profiles and shapelets. Requiring very little human interaction, our pixel-based technique fits into the effort to devise automated modelling schemes. It can be incorporated in the standard workflow of sampling analytical lens model parameters. The method, which we call SLITronomy, is freely available as a new plug-in to the modelling software Lenstronomy.