Measuring the Substructure Mass Power Spectrum of 23 SLACS Strong Galaxy-Galaxy Lenses with Convolutional Neural Networks

Abstract

Strong gravitational lensing can be used as a tool for constraining the substructure in the mass distribution of galaxies. In this study we investigate the power spectrum of dark matter perturbations in a population of 23 Hubble Space Telescope images of strong galaxy-galaxy lenses selected from The Sloan Lens ACS (SLACS) survey. We model the dark matter substructure as a Gaussian Random Field perturbation on a smooth lens mass potential, characterized by power-law statistics. We expand upon the previously developed machine learning framework to predict the power-law statistics by using a convolutional neural network (CNN) that accounts for both epistemic and aleatoric uncertainties. For the training sets, we use the smooth lens mass potentials and reconstructed source galaxies that have been previously modelled through traditional fits of analytical and shapelet profiles as a starting point. We train three CNNs with different training set: the first using standard data augmentation on the best-fitting reconstructed sources, the second using different reconstructed sources spaced throughout the posterior distribution, and the third using a combination of the two data sets. We apply the trained CNNs to the SLACS data and find agreement in their predictions. Our results suggest a significant substructure perturbation favoring a high frequency power spectrum across our lens population.

23 pages, 22 figures

References in corpus (33)

• Array Programming with NumPy
• The EAGLE project: Simulating the evolution and assembly of galaxies and their environments
• Ensemble deep learning: A review
• Properties of galaxies reproduced by a hydrodynamic simulation
• A Bayesian approach to strong lensing modelling of galaxy clusters
• Bayesian Strong Gravitational-Lens Modeling on Adaptive Grids: Objective Detection of Mass Substructure in Galaxies
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• lenstronomy II: A gravitational lensing software ecosystem
• Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
• Dark matter halos of massive elliptical galaxies at $z\sim 0.2$ are well described by the Navarro-Frenk-White profile
• PyAutoLens: Open-Source Strong Gravitational Lensing
• Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks
• Deep Generative Models for Galaxy Image Simulations
• Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing
• 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
• HOLISMOKES -- IV. Efficient mass modeling of strong lenses through deep learning
• Direct Detection of Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks
• Strong lensing in UNIONS: Toward a pipeline from discovery to modeling
• Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting
• Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation
• Detecting gravitational lenses using machine learning: exploring interpretability and sensitivity to rare lensing configurations
• SLITronomy: towards a fully wavelet-based strong lensing inversion technique
• Microlensing flux ratio predictions for Euclid
• Strong-lensing source reconstruction with variationally optimised Gaussian processes
• Quantifying the structure of strong gravitational lens potentials with uncertainty-aware deep neural networks
• Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines
• Probing sub-galactic mass structure with the power spectrum of surface-brightness anomalies in high-resolution observations of galaxy-galaxy strong gravitational lenses. I. Power-spectrum measurement and feasibility study
• Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses
• DrizzlePac 2.0 - Introducing New Features
• Strong Gravitational Lensing Parameter Estimation with Vision Transformer

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