Fast automated analysis of strong gravitational lenses with convolutional neural networks

Hezaveh, Yashar D.; Levasseur, Laurence Perreault; Marshall, Philip J.

doi:10.1038/nature23463

Title: Fast automated analysis of strong gravitational lenses with convolutional neural networks

Abstract

Quantifying image distortions caused by strong gravitational lensing—the formation of multiple images of distant sources due to the deflection of their light by the gravity of intervening structures—and estimating the corresponding matter distribution of these structures (the ‘gravitational lens’) has primarily been performed using maximum likelihood modelling of observations. Our procedure is typically time- and resource-consuming, 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 gravitational lens can take up to a few weeks and requires expert knowledge of the physical processes and methods involved. Tens of thousands of new lenses are expected to be discovered with the upcoming generation of ground and space surveys. We report the use of deep convolutional neural networks to estimate lensing parameters in an extremely fast and automated way, circumventing the difficulties that are faced by maximum likelihood methods. We also show that the removal of lens light 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, which is commonly used to model strong lensing systems, withmore »« less

Authors:

Hezaveh, Yashar D. ^[1]; Levasseur, Laurence Perreault ^[1]; Marshall, Philip J. ^[1]

Stanford Univ., CA (United States). Kavli Inst. for Particle Astrophysics and Cosmology; SLAC National Accelerator Lab., Menlo Park, CA (United States)

Publication Date:: Wed Aug 30 00:00:00 EDT 2017

Research Org.:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1390590

Grant/Contract Number:: AC02-76SF00515

Resource Type:: Accepted Manuscript

Journal Name:: Nature (London)

Additional Journal Information:: Journal Name: Nature (London); Journal Volume: 548; Journal Issue: 7669; Journal ID: ISSN 0028-0836

Publisher:: Nature Publishing Group

Country of Publication:: United States

Language:: English

Subject:: 79 ASTRONOMY AND ASTROPHYSICS; computational astrophysics; cosmology

Citation Formats


                    Hezaveh, Yashar D., Levasseur, Laurence Perreault, and Marshall, Philip J. Fast automated analysis of strong gravitational lenses with convolutional neural networks.  United States: N. p., 2017. 
Web.  doi:10.1038/nature23463.

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                    Hezaveh, Yashar D., Levasseur, Laurence Perreault, & Marshall, Philip J. Fast automated analysis of strong gravitational lenses with convolutional neural networks.  United States.  https://doi.org/10.1038/nature23463

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                    Hezaveh, Yashar D., Levasseur, Laurence Perreault, and Marshall, Philip J. Wed .  
"Fast automated analysis of strong gravitational lenses with convolutional neural networks".  United States.  https://doi.org/10.1038/nature23463.  https://www.osti.gov/servlets/purl/1390590.

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@article{osti_1390590,

  title        = {Fast automated analysis of strong gravitational lenses with convolutional neural networks},

  author       = {Hezaveh, Yashar D. and Levasseur, Laurence Perreault and Marshall, Philip J.},

  abstractNote = {Quantifying image distortions caused by strong gravitational lensing—the formation of multiple images of distant sources due to the deflection of their light by the gravity of intervening structures—and estimating the corresponding matter distribution of these structures (the ‘gravitational lens’) has primarily been performed using maximum likelihood modelling of observations. Our procedure is typically time- and resource-consuming, 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 gravitational lens can take up to a few weeks and requires expert knowledge of the physical processes and methods involved. Tens of thousands of new lenses are expected to be discovered with the upcoming generation of ground and space surveys. We report the use of deep convolutional neural networks to estimate lensing parameters in an extremely fast and automated way, circumventing the difficulties that are faced by maximum likelihood methods. We also show that the removal of lens light 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, which is 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 one second on a single graphics processing unit. These networks can provide a way for non-experts to obtain estimates of lensing parameters for large samples of data.},

  doi          = {10.1038/nature23463},

  journal      = {Nature (London)},

  number       = 7669,

  volume       = 548,

  place        = {United States},

  year         = {Wed Aug 30 00:00:00 EDT 2017},

  month        = {Wed Aug 30 00:00:00 EDT 2017}

}

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Conference Poh, Jason ; Samudre, Ashwin ; Ciprijanovic, Aleksandra ; ...

Current and future ground-based cosmological surveys, such as the Dark Energy Survey (DES), and the Vera Rubin Observatory Legacy Survey of Space and Time (LSST), are predicted to discover thousands to tens of thousands of strong gravitational lenses. The large number of strong lenses discoverable in future surveys will make strong lensing a highly competitive and complementary cosmic probe. However, conventional lens modeling techniques are unable to scale up to the sheer number of lenses that will be discovered through upcoming surveys. Therefore, the use of automated lens analysis techniques is necessary. We demonstrate that machine learning methods can bemore »« less
https://doi.org/10.2172/2246939

Full Text Available
A comparison of cosmological models using strong gravitational lensing galaxies

Journal Article Melia, Fulvio ; Wei, Jun-Jie ; Wu, Xue-Feng - Astronomical Journal (New York, N.Y. Online)

Strongly gravitationally lensed quasar-galaxy systems allow us to compare competing cosmologies as long as one can be reasonably sure of the mass distribution within the intervening lens. In this paper, we assemble a catalog of 69 such systems from the Sloan Lens ACS and Lens Structure and Dynamics surveys suitable for this analysis, and carry out a one-on-one comparison between the standard model, ΛCDM, and the R{sub h}=ct universe, which has thus far been favored by the application of model selection tools to other kinds of data. We find that both models account for the lens observations quite well, thoughmore »« less
https://doi.org/10.1088/0004-6256/149/1/2
Direct detection of dark matter substructure in strong lens images with convolutional neural networks

Journal Article Diaz Rivero, Ana ; Dvorkin, Cora - Physical Review D

Strong gravitational lensing is a promising way of uncovering the nature of dark matter, by finding perturbations to images that cannot be accounted for well by modeling the lens galaxy without additional structure, be it subhalos (smaller halos within the smooth lens) or line-of-sight (LOS) halos. We present results attempting to infer the presence of substructure from images without requiring an intermediate step in which a smooth model has to be subtracted, using a simple convolutional neural network (CNN). We find that the network is only able to infer the presence of subhalos with greater than 75% accuracy when theymore »« less
Cited by 16
https://doi.org/10.1103/PhysRevD.101.023515

Full Text Available
GOLDEN GRAVITATIONAL LENSING SYSTEMS FROM THE SLOAN LENS ACS SURVEY. I. SDSS J1538+5817: ONE LENS FOR TWO SOURCES ,

Journal Article Grillo, C ; Seitz, S ; Bender, R ; ... - Astrophysical Journal

We present a gravitational lensing and photometric study of the exceptional strong lensing system SDSS J1538+5817, identified by the Sloan Lens Advanced Camera for Survey. The lens is a luminous elliptical galaxy at redshift z{sub l} = 0.143. Using Hubble Space Telescope public images obtained with two different filters, the presence of two background sources lensed, respectively, into an Einstein ring and a double system is ascertained. Our new spectroscopic observations, performed at the Nordic Optical Telescope, reveal unequivocally that the two sources are located at the same redshift z{sub s} = 0.531. We investigate the total (luminous and dark)more »« less
https://doi.org/10.1088/0004-637X/710/1/372
Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation

Journal Article Ostdiek, Bryan ; Diaz Rivero, Ana ; Dvorkin, Cora - The Astrophysical Journal

Abstract Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it is a challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks to analyze each system. We use machine learning to circumvent the need for lens and source modeling and develop a neural network to both locate subhalos in an image as well as determine their mass using the technique of image segmentation. The network is trained on images with a single subhalo located near the Einstein ring across a wide range of apparent sourcemore »« less
https://doi.org/10.3847/1538-4357/ac2d8d

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Title: Fast automated analysis of strong gravitational lenses with convolutional neural networks

Abstract

Citation Formats

The Third Gravitational Lensing Accuracy Testing (GREAT3) Challenge Handbook text, January 2013

ImageNet Large Scale Visual Recognition Challenge text, January 2015

Galaxy Zoo: morphological classifications for 120 000 galaxies in HST legacy imaging journal, October 2016

A Bayesian analysis of regularized source inversions in gravitational lensing journal, September 2006

Finding strong lenses in CFHTLS using convolutional neural networks text, January 2017

The sl2s Galaxy-Scale lens Sample. iii. lens Models, Surface Photometry, and Stellar Masses for the Final Sample journal, October 2013

The population of galaxy-galaxy strong lenses in forthcoming optical imaging surveys text, January 2015

ImageNet Large Scale Visual Recognition Challenge journal, April 2015

Approximation by superpositions of a sigmoidal function journal, December 1989

Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes journal, November 2016

The sl2s Galaxy-Scale lens Sample. v. dark Matter Halos and Stellar imf of Massive Early-Type Galaxies out to Redshift 0.8 journal, February 2015

A Bayesian analysis of regularized source inversions in gravitational lensing journal, September 2006

The SL2S Galaxy-scale Lens Sample. III. Lens Models, Surface Photometry and Stellar Masses for the final sample text, January 2013

Lens Models and Magnification maps of the six Hubble Frontier Fields Clusters journal, November 2014

The Third Gravitational Lensing Accuracy Testing (Great3) Challenge Handbook journal, April 2014

The SL2S Galaxy-scale Lens Sample. V. Dark Matter Halos and Stellar IMF of Massive Early-type Galaxies out to Redshift 0.8 text, January 2014

Lens models and magnification maps of the six Hubble Frontier Fields clusters text, January 2014

Finding strong lenses in CFHTLS using convolutional neural networks journal, June 2017

Galaxy Zoo: Morphological Classifications for 120,000 Galaxies in HST Legacy Imaging text, January 2016

The Sloan Lens ACS Survey. V. The Full ACS Strong‐Lens Sample journal, August 2008

A Bayesian analysis of regularised source inversions in gravitational lensing text, January 2006

Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes text, January 2016

A Systematic Review of Strong Gravitational Lens Modeling Software text, January 2012

Semilinear Gravitational Lens Inversion journal, June 2003

Approximation by superpositions of a sigmoidal function journal, December 1992

A systematic review of strong gravitational lens modeling software journal, July 2013

The Population of Galaxy–Galaxy Strong Lenses in Forthcoming Optical Imaging Surveys journal, September 2015

CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding journal, July 2017

Enabling Dark Energy Science with Deep Generative Models of Galaxy Images journal, February 2017

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning journal, February 2017

Is every strong lens model unhappy in its own way? Uniform modelling of a sample of 13 quadruply+ imaged quasars text, January 2018

Probing the universe with citizen science and gravitational lensing dissertation, January 2017

Deep learning predictions of galaxy merger stage and the importance of observational realism journal, October 2019

Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan" preprint, January 2019

Neural network-based anomaly detection for high-resolution X-ray spectroscopy journal, June 2019

Deep-CEE I: fishing for galaxy clusters with deep neural nets journal, October 2019

X-ray Study of Spatial Structures in Tycho's Supernova Remnant Using Unsupervised Deep Learning text, January 2019

Deep learning predictions of galaxy merger stage and the importance of observational realism text, January 2019

Searching for Subsecond Stellar Variability with Wide-field Star Trails and Deep Learning journal, November 2018

Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning journal, October 2019

Optical Deep Learning Nano-Profilometry text, January 2019

Finding high-redshift strong lenses in DES using convolutional neural networks text, January 2018

Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers preprint, January 2020

Learning to Predict the Cosmological Structure Formation text, January 2018

Neural network-based preprocessing to estimate the parameters of the X-ray emission of a single-temperature thermal plasma text, January 2018

Using convolutional neural networks to predict galaxy metallicity from three-colour images journal, February 2019

Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia text, January 2019

Radio Galaxy Zoo: Claran – a deep learning classifier for radio morphologies journal, October 2018

Discovery of CDK4 inhibitors by convolutional neural networks journal, February 2019

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists journal, November 2018

Phased microphone array for sound source localization with deep learning journal, May 2019

Lessons from a blind study of simulated lenses: image reconstructions do not always reproduce true convergence text, January 2019

A trans-disciplinary review of deep learning research for water resources scientists text, January 2017

Automated Assignment of Rotational Spectra Using Artificial Neural Networks conference, June 2018

Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning journal, November 2019

Deep learning reconstruction of ultrashort pulses journal, January 2018

Image Simulations for Strong and Weak Gravitational Lensing journal, March 2020

A Bayesian neural network predicts the dissolution of compact planetary systems text, January 2021

Neural network-based preprocessing to estimate the parameters of the X-ray emission of a single-temperature thermal plasma journal, February 2018

Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning text, January 2019

Learning to predict the cosmological structure formation journal, June 2019

Machine Learning Detection of Bell Nonlocality in Quantum Many-Body Systems journal, June 2018

A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling preprint, January 2019

Seeing permeability from images: fast prediction with convolutional neural networks journal, September 2018

Phased microphone array for sound source localization with deep learning journal, May 2019

Lenstronomy: multi-purpose gravitational lens modelling software package text, January 2018

Machine learning and the physical sciences journal, December 2019

Deep Learning Reconstruction of Ultra-Short Pulses preprint, January 2018

A new era in the search for dark matter journal, October 2018

Lessons from a blind study of simulated lenses: image reconstructions do not always reproduce true convergence journal, January 2020

Direct detection of dark matter substructure in strong lens images with convolutional neural networks journal, January 2020

SWIRL: High-performance many-core CPU code generation for deep neural networks journal, May 2019

An Extended Catalog of Galaxy–Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks journal, July 2019

Learning to predict the cosmological structure formation journal, June 2019

Machine learning meets quantum foundations: A brief survey journal, October 2020

Machine Learning Bell Nonlocality in Quantum Many-body Systems text, January 2017

Machine Learning for Nanohertz Gravitational Wave Detection and Parameter Estimation with Pulsar Timing Array preprint, January 2020

The Third Gravitational Lensing Accuracy Testing (GREAT3) Challenge Handbook
text, January 2013

ImageNet Large Scale Visual Recognition Challenge
text, January 2015

Galaxy Zoo: morphological classifications for 120 000 galaxies in HST legacy imaging
journal, October 2016

A Bayesian analysis of regularized source inversions in gravitational lensing
journal, September 2006

Finding strong lenses in CFHTLS using convolutional neural networks
text, January 2017

The sl2s Galaxy-Scale lens Sample. iii. lens Models, Surface Photometry, and Stellar Masses for the Final Sample
journal, October 2013

The population of galaxy-galaxy strong lenses in forthcoming optical imaging surveys
text, January 2015

ImageNet Large Scale Visual Recognition Challenge
journal, April 2015

Approximation by superpositions of a sigmoidal function
journal, December 1989

Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes
journal, November 2016

The sl2s Galaxy-Scale lens Sample. v. dark Matter Halos and Stellar imf of Massive Early-Type Galaxies out to Redshift 0.8
journal, February 2015

A Bayesian analysis of regularized source inversions in gravitational lensing
journal, September 2006

The SL2S Galaxy-scale Lens Sample. III. Lens Models, Surface Photometry and Stellar Masses for the final sample
text, January 2013

Lens Models and Magnification maps of the six Hubble Frontier Fields Clusters
journal, November 2014

The Third Gravitational Lensing Accuracy Testing (Great3) Challenge Handbook
journal, April 2014

The SL2S Galaxy-scale Lens Sample. V. Dark Matter Halos and Stellar IMF of Massive Early-type Galaxies out to Redshift 0.8
text, January 2014

Lens models and magnification maps of the six Hubble Frontier Fields clusters
text, January 2014

Finding strong lenses in CFHTLS using convolutional neural networks
journal, June 2017

Galaxy Zoo: Morphological Classifications for 120,000 Galaxies in HST Legacy Imaging
text, January 2016

The Sloan Lens ACS Survey. V. The Full ACS Strong‐Lens Sample
journal, August 2008

A Bayesian analysis of regularised source inversions in gravitational lensing
text, January 2006

Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes
text, January 2016

A Systematic Review of Strong Gravitational Lens Modeling Software
text, January 2012

Semilinear Gravitational Lens Inversion
journal, June 2003

Approximation by superpositions of a sigmoidal function
journal, December 1992

A systematic review of strong gravitational lens modeling software
journal, July 2013

The Population of Galaxy–Galaxy Strong Lenses in Forthcoming Optical Imaging Surveys
journal, September 2015

CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding
journal, July 2017

Enabling Dark Energy Science with Deep Generative Models of Galaxy Images
journal, February 2017

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
journal, February 2017

Is every strong lens model unhappy in its own way? Uniform modelling of a sample of 13 quadruply+ imaged quasars
text, January 2018

Probing the universe with citizen science and gravitational lensing
dissertation, January 2017

Deep learning predictions of galaxy merger stage and the importance of observational realism
journal, October 2019

Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan"
preprint, January 2019

Neural network-based anomaly detection for high-resolution X-ray spectroscopy
journal, June 2019

Deep-CEE I: fishing for galaxy clusters with deep neural nets
journal, October 2019

X-ray Study of Spatial Structures in Tycho's Supernova Remnant Using Unsupervised Deep Learning
text, January 2019

Deep learning predictions of galaxy merger stage and the importance of observational realism
text, January 2019

Searching for Subsecond Stellar Variability with Wide-field Star Trails and Deep Learning
journal, November 2018

Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning
journal, October 2019

Optical Deep Learning Nano-Profilometry
text, January 2019

Finding high-redshift strong lenses in DES using convolutional neural networks
text, January 2018

Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers
preprint, January 2020

Learning to Predict the Cosmological Structure Formation
text, January 2018

Neural network-based preprocessing to estimate the parameters of the X-ray emission of a single-temperature thermal plasma
text, January 2018

Using convolutional neural networks to predict galaxy metallicity from three-colour images
journal, February 2019

Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia
text, January 2019

Radio Galaxy Zoo: Claran – a deep learning classifier for radio morphologies
journal, October 2018

Discovery of CDK4 inhibitors by convolutional neural networks
journal, February 2019

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists
journal, November 2018

Phased microphone array for sound source localization with deep learning
journal, May 2019

Lessons from a blind study of simulated lenses: image reconstructions do not always reproduce true convergence
text, January 2019

A trans-disciplinary review of deep learning research for water resources scientists
text, January 2017

Automated Assignment of Rotational Spectra Using Artificial Neural Networks
conference, June 2018

Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning
journal, November 2019

Deep learning reconstruction of ultrashort pulses
journal, January 2018

Image Simulations for Strong and Weak Gravitational Lensing
journal, March 2020

A Bayesian neural network predicts the dissolution of compact planetary systems
text, January 2021

Neural network-based preprocessing to estimate the parameters of the X-ray emission of a single-temperature thermal plasma
journal, February 2018

Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
text, January 2019

Learning to predict the cosmological structure formation
journal, June 2019

Machine Learning Detection of Bell Nonlocality in Quantum Many-Body Systems
journal, June 2018

A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling
preprint, January 2019

Seeing permeability from images: fast prediction with convolutional neural networks
journal, September 2018

Phased microphone array for sound source localization with deep learning
journal, May 2019

Lenstronomy: multi-purpose gravitational lens modelling software package
text, January 2018

Machine learning and the physical sciences
journal, December 2019

Deep Learning Reconstruction of Ultra-Short Pulses
preprint, January 2018

A new era in the search for dark matter
journal, October 2018

Lessons from a blind study of simulated lenses: image reconstructions do not always reproduce true convergence
journal, January 2020

Direct detection of dark matter substructure in strong lens images with convolutional neural networks
journal, January 2020

SWIRL: High-performance many-core CPU code generation for deep neural networks
journal, May 2019

An Extended Catalog of Galaxy–Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks
journal, July 2019

Learning to predict the cosmological structure formation
journal, June 2019

Machine learning meets quantum foundations: A brief survey
journal, October 2020

Machine Learning Bell Nonlocality in Quantum Many-body Systems
text, January 2017

Machine Learning for Nanohertz Gravitational Wave Detection and Parameter Estimation with Pulsar Timing Array
preprint, January 2020

Regularized pseudo-phase imaging for inspecting and sensing nanoscale features
journal, January 2019

Automated assignment of rotational spectra using artificial neural networks
journal, September 2018

Genetic-algorithm-optimized neural networks for gravitational wave classification
text, January 2020