Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses

Abstract

Strong gravitational lensing is a unique observational tool for studying the dark and luminous mass distribution both within and between galaxies. Given the presence of substructures, current strong lensing observations demand more complex mass models than smooth analytical profiles, such as power-law ellipsoids. In this work, we introduce a continuous neural field to predict the lensing potential at any position throughout the image plane, allowing for a nearly model-independent description of the lensing mass. We apply our method on simulated Hubble Space Telescope imaging data containing different types of perturbations to a smooth mass distribution: a localized dark subhalo, a population of subhalos, and an external shear perturbation. Assuming knowledge of the source surface brightness, we use the continuous neural field to model either the perturbations alone or the full lensing potential. In both cases, the resulting model is able to fit the imaging data, and we are able to accurately recover the properties of both the smooth potential and of the perturbations. Unlike many other deep learning methods, ours explicitly retains lensing physics (i.e., the lens equation) and introduces high flexibility in the model only where required, namely, in the lens potential. Moreover, the neural network does not require pre-training on large sets of labelled data and predicts the potential from the single observed lensing image. Our model is implemented in the fully differentiable lens modeling code Herculens.

References in corpus (15)

• Too big to fail? The puzzling darkness of massive Milky Way subhaloes
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• Effects of Supernova Feedback on the Formation of Galaxy Disks
• Is there a "too big to fail" problem in the field?
• Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
• Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
• The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples
• TDCOSMO. VII. Boxyness/discyness in lensing galaxies : Detectability and impact on $H_0$
• From Images to Dark Matter: End-To-End Inference of Substructure From Hundreds of Strong Gravitational Lenses
• The inner mass power spectrum of galaxies using strong gravitational lensing: beyond linear approximation
• Direct Detection of Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks
• GIGA-Lens: Fast Bayesian Inference for Strong Gravitational Lens Modeling
• Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses
• The Very Knotty Lenser: exploring the role of regularization in source and potential reconstructions using Gaussian Process Regression
• SLITronomy: towards a fully wavelet-based strong lensing inversion technique

Cited by in corpus (5)

• Strong Lensing by Galaxies
• Measuring the Substructure Mass Power Spectrum of 23 SLACS Strong Galaxy-Galaxy Lenses with Convolutional Neural Networks
• A compact group lens modeled with GIGA-Lens: Enhanced inference for complex systems
• Source REconstruction of Arcs behind Cluster Halos (SourceREACH): A New Source Reconstruction Algorithm Optimized for Giant Arcs and Galaxy Cluster Lenses
• A short review on joint weak and strong cluster lens-mass reconstruction