This repository gathers notebooks and resources to reproduce the results of Galan et al. 2021 and Galan et al. 2022.
Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses: Galan et al. 2022
We introduce a forward pixelated method to reconstruct perturbations to a smooth lens potential on a grid of pixels. The model is regularized using a well-motivated multi-scale approach based on wavelet transforms and sparsity constraints.
We test and validate the method on three very different types of perturbations:
- localized dark subhalo ("LS")
- population of substructures along the line of sight ("PS")
- high-order multipoles in the lens galaxy ("HM")
Note: to run the notebooks in the paper_II directory, it is advised to use the paper2 branch from Herculens.
This works also releases a new open-source modeling software package. Called Herculens, its main feature is the exact computation of the gradient and higher-order derivatives of the loss function based on JAX. This enables robust convergence to the solution, fast estimation of parameter covariances, and improved sampling in high-dimensional parameter space.
SLITronomy: Towards a fully wavelet-based strong lensing inversion technique: Galan et al. 2021
Introduction of an optimised implementation of the SLIT algorithms: SLITronomy. It is easily accessible through the lenstronomy package. We show applications on both simulated and real HST data, and anticipate the requirements of future E-ELT imaging data.
Source reconstruction for the system SDSSJ1250+0523, assuming lens model of Shajib et al. 2020.
Source reconstruction for mock E-ELT data, assuming known lens model.
Joint source reconstruction and lens model posterior inference from mock HST data. The posterior sampling is performed through MCMC capabilities of lenstronomy, and marginalised over choices of the source pixel size.
