Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses by Aymeric Galan et al. on Wednesday 23 November

Modeling the mass distribution of galaxy-scale strong gravitational lenses is

a task of increasing difficulty. The high-resolution and depth of imaging data

now available render simple analytical forms ineffective at capturing lens

structures spanning a large range in spatial scale, mass scale, and morphology.

In this work, we address the problem with a novel multiscale method based on

wavelets. We tested our method on simulated Hubble Space Telescope (HST)

imaging data of strong lenses containing the following different types of mass

substructures making them deviate from smooth models: (1) a localized small

dark matter subhalo, (2) a Gaussian random field (GRF) that mimics a

nonlocalized population of subhalos along the line of sight, and (3)

galaxy-scale multipoles that break elliptical symmetry. We show that wavelets

are able to recover all of these structures accurately. This is made

technically possible by using gradient-informed optimization based on automatic

differentiation over thousands of parameters, which also allow us to sample the

posterior distributions of all model parameters simultaneously. By

construction, our method merges the two main modeling paradigms - analytical

and pixelated - with machine-learning optimization techniques into a single

modular framework. It is also well-suited for the fast modeling of large

samples of lenses. All methods presented here are publicly available in our new

Herculens package.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2207.05763v2