Strong-lensing source reconstruction with variationally optimised Gaussian processes

Abstract

Strong-lensing images provide a wealth of information both about the magnified source and about the dark matter distribution in the lens. Precision analyses of these images can be used to constrain the nature of dark matter. However, this requires high-fidelity image reconstructions and careful treatment of the uncertainties of both lens mass distribution and source light, which are typically difficult to quantify. In anticipation of future high-resolution datasets, in this work we leverage a range of recent developments in machine learning to develop a new Bayesian strong-lensing image analysis pipeline. Its highlights are: (A) a fast, GPU-enabled, end-to-end differentiable strong-lensing image simulator; (B) a new, statistically principled source model based on a computationally highly efficient approximation to Gaussian processes that also takes into account pixellation; and (C) a scalable variational inference framework that enables simultaneously deriving posteriors for tens of thousands of lens and source parameters and optimising hyperparameters via stochastic gradient descent. Besides efficient and accurate parameter estimation and lens model uncertainty quantification, the main aim of the pipeline is the generation of training data for targeted simulation-based inference of dark matter substructure, which we will exploit in a companion paper.

arXiv comment24 pages, 20 figures

References in corpus (60)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Array Programming with NumPy
• Astropy: A Community Python Package for Astronomy
• Planck 2015 results. XIII. Cosmological parameters
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• A History of Dark Matter
• H0LiCOW XIII. A 2.4% measurement of $H_0$ from lensed quasars: $5.3\sigma$ tension between early and late-Universe probes
• The frontier of simulation-based inference
• Fast and scalable Gaussian process modeling with applications to astronomical time series
• Planck 2018 results. VIII. Gravitational lensing
• The Sloan Lens ACS Survey. I. A Large Spectroscopically Selected Sample of Massive Early-Type Lens Galaxies
• Microlensing constraints on primordial black holes with the Subaru/HSC Andromeda observation
• The Sloan Lens ACS Survey. V. The Full ACS Strong-Lens Sample
• The Cosmic Lens All-Sky Survey: I. Source selection and observations
• Gravitational detection of a low-mass dark satellite at cosmological distance
• The population of galaxy-galaxy strong lenses in forthcoming optical imaging surveys
• Detection of lensing substructure using ALMA observations of the dusty galaxy SDP.81
• Lenstronomy: multi-purpose gravitational lens modelling software package
• Detection of a Dark Substructure through Gravitational Imaging
• Bayesian Strong Gravitational-Lens Modeling on Adaptive Grids: Objective Detection of Mass Substructure in Galaxies
• Warm dark matter chills out: constraints on the halo mass function and the free-streaming length of dark matter with 8 quadruple-image strong gravitational lenses
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• Lyman-alpha Emission from a Luminous z=8.68 Galaxy: Implications for Galaxies as Tracers of Cosmic Reionization
• A Bayesian analysis of regularised source inversions in gravitational lensing
• Semi-linear gravitational lens inversion
• Gravitational Lens Models Based on Submillimeter Array Imaging of Herschel-selected Strongly Lensed Sub-millimeter Galaxies at z>1.5
• ALMA Imaging and Gravitational Lens Models of South Pole Telescope-Selected Dusty, Star-Forming Galaxies at High Redshifts
• The BOSS Emission-Line Lens Survey (BELLS). I. A large spectroscopically selected sample of Lens Galaxies at redshift ~ 0.5
• SHARP -- VII. New constraints on the dark matter free-streaming properties and substructure abundance from gravitationally lensed quasars
• The CFHTLS-Strong Lensing Legacy Survey (SL2S): Investigating the group-scale lenses with the SARCS sample
• Gravitational-wave parameter estimation with autoregressive neural network flows
• Gravitational lens modeling with basis sets
• Source-position transformation -- an approximate invariance in strong gravitational lensing
• The alignment and shape of dark matter, stellar, and hot gas distributions in the EAGLE and cosmo-OWLS simulations
• AutoLens: Automated Modeling of a Strong Lens's Light, Mass and Source
• Statistical 21-cm Signal Separation via Gaussian Process Regression Analysis
• Two-dimensional kinematics of SLACS lenses: II. Combined lensing and dynamics analysis of early-type galaxies at z = 0.08 - 0.33
• Low-mass halo perturbations in strong gravitational lenses at redshift z$\sim$0.5 are consistent with CDM
• Lensing substructure quantification in RXJ1131-1231: A 2 keV lower bound on dark matter thermal relic mass
• Dissecting the Gravitational Lens B1608+656. I. Lens Potential Reconstruction
• Decomposition of the visible and dark matter in the Einstein ring 0047-2808 by semi-linear inversion
• The Sloan Lens ACS Survey. XIII. Discovery of 40 New Galaxy-Scale Strong Lenses
• Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey
• The elliptical power law profile lens
• Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
• The BOSS Emission-Line Lens Survey. III. : Strong Lensing of Ly$\alpha$ Emitters by Individual Galaxies
• Data-Driven Reconstruction of Gravitationally Lensed Galaxies using Recurrent Inference Machines
• A Unifying Framework for Self-consistent Gravitational Lensing and Stellar Dynamics Analyses of Early-Type Galaxies
• Microlensing Searches for Exoplanets
• Comparison of classical and Bayesian imaging in radio interferometry
• STACCATO: A Novel Solution to Supernova Photometric Classification with Biased Training Sets
• Differentiable Strong Lensing: Uniting Gravity and Neural Nets through Differentiable Probabilistic Programming
• Detecting Damped Lyman-$\alpha$ Absorbers with Gaussian Processes
• Wavefunction positivization via automatic differentiation
• SLITronomy: towards a fully wavelet-based strong lensing inversion technique
• The segregation of baryons and dark matter during halo assembly
• On the use of shapelets in modelling resolved, gravitationally lensed images
• Systematic errors in strong gravitational lensing reconstructions, a numerical simulation perspective
• Galaxy mass profiles from strong lensing III: The two-dimensional broken power-law model

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• 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
• Measuring the Substructure Mass Power Spectrum of 23 SLACS Strong Galaxy-Galaxy Lenses with Convolutional Neural Networks
• Analytic auto-differentiable $\Lambda$CDM cosmography
• Source REconstruction of Arcs behind Cluster Halos (SourceREACH): A New Source Reconstruction Algorithm Optimized for Giant Arcs and Galaxy Cluster Lenses