Carregant...
Miniatura

Tipus de document

Article

Versió

Versió publicada

Data de publicació

Tots els drets reservats

Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/221121

Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses

Títol de la revista

Director/Tutor

ISSN de la revista

Títol del volum

Resum

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.

Citació

Citació

GALAN, A., VERNARDOS, G., PEEL, A., COURBIN, Frédéric, STARCK, J.-l.. Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses. _Astronomy & Astrophysics_. 2022. Vol. 668, núm. A155. [consulta: 26 de febrer de 2026]. ISSN: 0004-6361. [Disponible a: https://hdl.handle.net/2445/221121]

Exportar metadades

JSON - METS

Compartir registre