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 multi-scale method based on
wavelets. We test our method on simulated Hubble Space Telescope imaging data
of strong lenses containing different types of mass substructures making them
deviate from smooth models: (1) a localized small dark matter subhalo, (2) a
Gaussian random field that mimics a non-localized population of subhalos along
the line of sight, (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, also allowing us to
sample the posterior distributions of all model parameters simultaneously. By
construction, our method merges all current modeling paradigms - analytical,
pixelated, and deep learning - 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.
Comment: 23 pages, 11 figures, submitted to A&A
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics; Astrophysics - Cosmology and Nongalactic Astrophysics; Astrophysics - Astrophysics of Galaxies