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

Abstract

In the era of large-scale astronomical surveys, 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. 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. We employ 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 allows us to overcome the challenge of limited prior information, a high number of parameters, and memory usage. Our process is exemplified through the analysis of the compact group lens system DES J0248-3955, for which we present VLT/X-shooter spectra. We measure a redshift of $z=0.69\pm 0.04$ for the group, and $z=1.2722\pm 0.0005$ 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 ${\sigma }_v=(690\pm 30)km{s}^{-1}$. The model predicts the presence of a second source at the same redshift and a third source at approximately $z\sim 2.7$. 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 considerable potential within the context of large surveys such as LSST.

10 pages, 6 figures. Submitted to A&A. The final version may differ slightly after production edits

References in corpus (29)

• A Bayesian approach to strong lensing modelling of galaxy clusters
• The Structure & Dynamics of Massive Early-type Galaxies: On Homology, Isothermality and Isotropy inside one Effective Radius
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• The Clustering of DESI-like Luminous Red Galaxies Using Photometric Redshifts
• lenstronomy II: A gravitational lensing software ecosystem
• Cosmological Constraints from Strong Gravitational Lensing in Galaxy Clusters
• Constraints on the Hubble constant from Supernova Refsdal's reappearance
• Discovering New Strong Gravitational Lenses in the DESI Legacy Imaging Surveys
• Arc sensitivity to cluster ellipticity, asymmetries and substructures
• The GLASS-JWST Early Release Science Program. III. Strong lensing model of Abell 2744 and its infalling regions
• Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks
• HOLISMOKES. VIII. High-redshift, strong-lens search in the Hyper Suprime-Cam Subaru Strategic Program
• The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples
• HOLISMOKES -- IV. Efficient mass modeling of strong lenses through deep learning
• Galaxy cluster strong lensing cosmography: cosmological constraints from a sample of regular galaxy clusters
• Differentiable Strong Lensing: Uniting Gravity and Neural Nets through Differentiable Probabilistic Programming
• Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting
• The Relation between Dynamical Mass-to-Light Ratio and Color for Massive Quiescent Galaxies out to z~2 and Comparison with Stellar Population Synthesis Models
• New Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9
• Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
• HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images
• The DES Bright Arcs Survey: Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey 5,000 Sq. Deg. Footprint
• Testing Cosmology with Double Source Lensing
• Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines
• Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses
• Revealing the Nature of a Lyman-$\alpha$ Halo in a Strongly Lensed Interacting System at $z=2.92$
• A Comparative Study of Convolutional Neural Networks for the Detection of Strong Gravitational Lensing
• DESI-253.2534+26.8843: A New Einstein Cross Spectroscopically Confirmed with VLT/MUSE and Modeled with GIGA-Lens
• Modeling Strong Lenses from Wide-Field Ground-Based Observations in KiDS and GAMA

Cited by in corpus (2)

• CURLING -- II. Improvement on the $H_0$ Inference from Pixelized Cluster Strong Lens Modeling
• Pixel-level modelling of group-scale strong lens CASSOWARY 19