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

¹ Institute of Astrophysics, Pontificia Universidad Católica de Chile, Santiago, Chile
² Aix Marseille Univ, CNRS, CNES, LAM, Marseille, France
³ Department of Physics & Astronomy, University of San Francisco, San Francisco, USA

^★ Corresponding author; fjurcelay@uc.cl

Received: 17 January 2024
Accepted: 14 December 2024

Abstract

Context. 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 σ_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 ~ 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.