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

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...

Descripción completa

Detalles Bibliográficos
Autores: Galan, A., Vernardos, G., Peel, A., Courbin, Frédéric, Starck, J.-L.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/221121
Acceso en línea:https://hdl.handle.net/2445/221121
Access Level:acceso abierto
Palabra clave:Galàxies
Matèria fosca (Astronomia)
Gravitació
Galaxies
Dark matter (Astronomy)
Gravitation
id ES_719b26eeb565fcf06db0f7d53c6b2dcf
oai_identifier_str oai:recercat.cat:2445/221121
network_acronym_str ES
network_name_str España
repository_id_str
spelling Using wavelets to capture deviations from smoothness in galaxy-scale strong lensesGalan, A.Vernardos, G.Peel, A.Courbin, FrédéricStarck, J.-L.GalàxiesMatèria fosca (Astronomia)GravitacióGalaxiesDark matter (Astronomy)GravitationModeling 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.EDP Sciences2025202520222025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion24 p.application/pdfhttps://hdl.handle.net/2445/221121Articles publicats en revistes (Institut de Ciències del Cosmos (ICCUB))reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1051/0004-6361/202244464Astronomy & Astrophysics, 2022, vol. 668, num.A155https://doi.org/10.1051/0004-6361/202244464(c) The European Southern Observatory (ESO), 2022info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2211212026-05-29T05:05:01Z
dc.title.none.fl_str_mv Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses
title Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses
spellingShingle Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses
Galan, A.
Galàxies
Matèria fosca (Astronomia)
Gravitació
Galaxies
Dark matter (Astronomy)
Gravitation
title_short Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses
title_full Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses
title_fullStr Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses
title_full_unstemmed Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses
title_sort Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses
dc.creator.none.fl_str_mv Galan, A.
Vernardos, G.
Peel, A.
Courbin, Frédéric
Starck, J.-L.
author Galan, A.
author_facet Galan, A.
Vernardos, G.
Peel, A.
Courbin, Frédéric
Starck, J.-L.
author_role author
author2 Vernardos, G.
Peel, A.
Courbin, Frédéric
Starck, J.-L.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Galàxies
Matèria fosca (Astronomia)
Gravitació
Galaxies
Dark matter (Astronomy)
Gravitation
topic Galàxies
Matèria fosca (Astronomia)
Gravitació
Galaxies
Dark matter (Astronomy)
Gravitation
description 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/221121
url https://hdl.handle.net/2445/221121
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1051/0004-6361/202244464
Astronomy & Astrophysics, 2022, vol. 668, num.A155
https://doi.org/10.1051/0004-6361/202244464
dc.rights.none.fl_str_mv (c) The European Southern Observatory (ESO), 2022
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) The European Southern Observatory (ESO), 2022
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 24 p.
application/pdf
dc.publisher.none.fl_str_mv EDP Sciences
publisher.none.fl_str_mv EDP Sciences
dc.source.none.fl_str_mv Articles publicats en revistes (Institut de Ciències del Cosmos (ICCUB))
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869410664662958080
score 15.812429

Similares en LA Referencia