Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks

Abstract

Quantifying image distortions caused by strong gravitational lensing and estimating the corresponding matter distribution in lensing galaxies has been primarily performed by maximum likelihood modeling of observations. This is typically a time and resource-consuming procedure, requiring sophisticated lensing codes, several data preparation steps, and finding the maximum likelihood model parameters in a computationally expensive process with downhill optimizers. Accurate analysis of a single lens can take up to a few weeks and requires the attention of dedicated experts. Tens of thousands of new lenses are expected to be discovered with the upcoming generation of ground and space surveys, the analysis of which can be a challenging task. Here we report the use of deep convolutional neural networks to accurately estimate lensing parameters in an extremely fast and automated way, circumventing the difficulties faced by maximum likelihood methods. We also show that lens removal can be made fast and automated using Independent Component Analysis of multi-filter imaging data. Our networks can recover the parameters of the Singular Isothermal Ellipsoid density profile, commonly used to model strong lensing systems, with an accuracy comparable to the uncertainties of sophisticated models, but about ten million times faster: 100 systems in approximately 1s on a single graphics processing unit. These networks can provide a way for non-experts to obtain lensing parameter estimates for large samples of data. Our results suggest that neural networks can be a powerful and fast alternative to maximum likelihood procedures commonly used in astrophysics, radically transforming the traditional methods of data reduction and analysis.

Accepted for publication in Nature. Under press embargo till 18:00 GMT (1300 US Eastern Time) on Wednesday August 30th

References in corpus (4)

• The Sloan Lens ACS Survey. V. The Full ACS Strong-Lens Sample
• Lens models and magnification maps of the six Hubble Frontier Fields clusters
• The SL2S Galaxy-scale Lens Sample. V. Dark Matter Halos and Stellar IMF of Massive Early-type Galaxies out to Redshift 0.8
• Galaxy Zoo: Morphological Classifications for 120,000 Galaxies in HST Legacy Imaging

Cited by in corpus (83)

• Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
• Deep Reinforcement Learning for Quantum Gate Control
• Deep learning predictions of galaxy merger stage and the importance of observational realism
• Machine learning technique to find quantum many-body ground states of bosons on a lattice
• Photometric Redshifts for Next-Generation Surveys
• The DAWES review 10: The impact of deep learning for the analysis of galaxy surveys
• Strong lensing time-delay cosmography in the 2020s
• Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
• STRIDES: Automated uniform models for 30 quadruply imaged quasars
• Nuclear liquid-gas phase transition with machine learning
• Automated galaxy-galaxy strong lens modelling: no lens left behind
• The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples
• Direct Detection of Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks
• Machine Learning for Observational Cosmology
• Sensitivity of strong lensing observations to dark matter substructure: a case study with Euclid
• Determining the temperature in heavy-ion collisions with multiplicity distribution
• TDCOSMO X. Automated Modeling of 9 Strongly Lensed Quasars and Comparison Between Lens Modeling Software
• Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant
• Strong Lensing by Galaxies
• 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
• Morphological classification of compact and extended radio galaxies using convolutional neural networks and data augmentation techniques
• Quasar microlensing light curve analysis using deep machine learning
• Recovering the CMB Signal with Machine Learning
• SLITronomy: towards a fully wavelet-based strong lensing inversion technique
• A machine learning based approach to gravitational lens identification with the International LOFAR Telescope
• LeHaMoC: a versatile time-dependent lepto-hadronic modeling code for high-energy astrophysical sources
• Likelihood-free Inference with Mixture Density Network
• Neural network-based anomaly detection for high-resolution X-ray spectroscopy
• Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers
• A 4% measurement of $H_0$ using the cumulative distribution of strong-lensing time delays in doubly-imaged quasars
• Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
• Streamlined Lensed Quasar Identification in Multiband Images via Ensemble Networks
• Auto-identification of unphysical source reconstructions in strong gravitational lens modelling
• HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images
• HOLISMOKES -- X. Comparison between neural network and semi-automated traditional modeling of strong lenses
• LeMoN: Lens Modelling with Neural networks -- I. Automated modelling of strong gravitational lenses with Bayesian Neural Networks
• Machine Learning and Cosmology
• A Framework for Obtaining Accurate Posteriors of Strong Gravitational Lensing Parameters with Flexible Priors and Implicit Likelihoods using Density Estimation
• PGNets: Planet mass prediction using convolutional neural networks for radio continuum observations of protoplanetary disks
• Models of gravitational lens candidates from Space Warps CFHTLS
• A Deep Learning Method for AGILE-GRID GRB Detection
• HOLISMOKES -- XI. Evaluation of supervised neural networks for strong-lens searches in ground-based imaging surveys
• Application of Deep Learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM TTE Data
• Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines
• Deep Learning in Wide-field Surveys: Fast Analysis of Strong Lenses in Ground-based Cosmic Experiments
• Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses
• Targeted Likelihood-Free Inference of Dark Matter Substructure in Strongly-Lensed Galaxies
• X-ray Study of Spatial Structures in Tycho's Supernova Remnant Using Unsupervised Deep Learning
• HOLISMOKES XIII: Strong-lens candidates at all mass scales and their environments from the Hyper-Suprime Cam and deep learning
• Simple lessons from complex learning: what a neural network model learns about cosmic structure formation
• Developing a Victorious Strategy to the Second Strong Gravitational Lensing Data Challenge
• Discover the GellMann-Okubo formula with machine learning
• Euclid preparation. LI. Forecasting the recovery of galaxy physical properties and their relations with template-fitting and machine-learning methods
• Cosmological joint analysis with cosmic growth and expansion rate
• Anisotropic strong lensing as a probe of dark matter self-interactions
• Lens Modeling of STRIDES Strongly Lensed Quasars using Neural Posterior Estimation
• Dark Matter Subhalos, Strong Lensing and Machine Learning
• A proof-of-concept neural network for inferring parameters of a black hole from partial interferometric images of its shadow
• Hunting for Dark Matter Subhalos in Strong Gravitational Lensing with Neural Networks
• YOLO-CL cluster detection in the Rubin/LSST DC2 simulation
• Measuring the Substructure Mass Power Spectrum of 23 SLACS Strong Galaxy-Galaxy Lenses with Convolutional Neural Networks
• kSZ Pairwise Velocity Reconstruction with Machine Learning
• Accelerating lensed quasar discovery and modeling with physics-informed variational autoencoders
• A compact group lens modeled with GIGA-Lens: Enhanced inference for complex systems
• Datacube segmentation via Deep Spectral Clustering
• Neural Scaling Laws for Deep Regression
• Femtosecond pulse parameter estimation from photoelectron momenta using machine learning
• Gaia GraL: Gaia gravitational lens systems IX. Using XGBoost to explore the Gaia Focused Product Release GravLens catalogue
• Constraining primordial non-Gaussianity using Neural Networks
• Success of Machine Learning algorithms in Dynamical Mass Measurements of Galaxy Clusters
• Understanding the predication mechanism of deep learning through error propagation among parameters in strong lensing case
• Deep inference of simulated strong lenses in ground-based surveys
• Determining the Dark Matter distribution in galaxies with Deep Learning
• Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra
• From Dark Matter Minihalos to Large-Scale Radiative Feedback: A Self-Consistent 3D Simulation of the First Stars and Galaxies using Neural Networks
• A short review on joint weak and strong cluster lens-mass reconstruction
• Enhancing Gravitational Lens Study with Deep Learning: A Study on Effects of Dropout Regularization
• Multi-frequency point source detection with fully convolutional networks: Performance in realistic microwave sky simulations
• A New Method for the Reconstruction of Strongly Lensed Galaxies with Resolved Kinematics
• Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
• Introduction to Machine Learning for Accelerator Physics
• Differentiable probabilistic programming for strong gravitational lensing