A compact group lens modeled with GIGA-Lens: Enhanced inference for complex systems

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dc.article.numberA35
dc.catalogadorvzp
dc.contributor.authorUrcelay Solis De Ovando, Felipe José
dc.contributor.authorJullo, Eric
dc.contributor.authorBarrientos Parra, Luis Felipe
dc.contributor.authorHuang, Xiaosheng
dc.contributor.authorHernández Guajardo, Joaquín Alexis
dc.date.accessioned2025-03-17T18:48:09Z
dc.date.available2025-03-17T18:48:09Z
dc.date.issued2025
dc.description.abstractContext. In the era of large-scale astronomical surveys, the fast modeling of strong lens systems has become increasingly vital. While significant progress has been made for galaxy-scale lenses, the development of automated methods for modeling larger systems, such as groups and clusters, is not as extensive., Aims. Our study aims to extend the capabilities of the GIGA-Lens code, enhancing its efficiency in modeling multi-galaxy strong lens systems. We focus on demonstrating the potential of GPU-accelerated Bayesian inference in handling complex lensing scenarios with a high number of free parameters., Methods. We employed an improved inference approach that combines image position and pixelated data with an annealing sampling technique to obtain the posterior distribution of complex models. This method allowed us to overcome the challenges of limited prior information, a high number of parameters, and memory usage. We validated our process through the analysis of the compact group lens system DES J0248-3955 and we present the relevant VLT/X-shooter spectra., Results. We measured a redshift of z = 0.69 +/- 0.04 for the group, and z = 1.2722 +/- 0.0005 for one of the extended arcs. Our enhanced method successfully constrained a lens model with 29 free parameters and lax priors in a remarkably short time. The mass of the lens is well described by a single dark-matter halo with a velocity dispersion of sigma(v) = (690 +/- 30) km s(-1). The model predicts the presence of a second source at the same redshift and a third source at approximately z similar to 2.7., Conclusions. Our study demonstrates the effectiveness of our lens modeling technique for dealing with a complex system in a short time using ground-based data. This presents a considerable prospect within the context of large surveys, such as LSST, in the future.
dc.description.funderANID/FONDECYT; Folio de beca: FB210003 y 23050313
dc.fechaingreso.objetodigital2025-03-17
dc.format.extent10 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1051/0004-6361/202449261
dc.identifier.eissn1432-0746
dc.identifier.issn0004-6361
dc.identifier.urihttps://doi.org/10.1051/0004-6361/202449261
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/102682
dc.identifier.wosidWOS:001410983200010
dc.information.autorucInstituto de Astrofísica; Urcelay Solis De Ovando, Felipe José; S/I; 247050
dc.information.autorucInstituto de Astrofísica; Barrientos Parra, Luis Felipe; 0000-0003-0151-0718; 102167
dc.information.autorucInstituto de Astrofísica; Hernández Guajardo, Joaquín Alexis; S/I; 1064498
dc.language.isoen
dc.nota.accesocontenido completo
dc.publisherEDP SCIENCES S A
dc.revistaASTRONOMY & ASTROPHYSICS
dc.rightsacceso abierto
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectGravitational lensing: strong
dc.subjectMethods: data analysis
dc.subjectGalaxies: groups: individual: DES J0248-3955
dc.subject.ddc520
dc.subject.deweyAstronomíaes_ES
dc.subject.ods09 Industry, innovation and infrastructure
dc.subject.odspa09 Industria, innovación e infraestructura
dc.titleA compact group lens modeled with GIGA-Lens: Enhanced inference for complex systems
dc.typeartículo
dc.volumen694
sipa.codpersvinculados247050
sipa.codpersvinculados102167
sipa.codpersvinculados1064498
sipa.trazabilidadWOS;2025-02-15
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