Euclid Preparation XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong lensing events

Abstract

Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions of galaxies will have to be inspected to identify potential candidates. In this context, deep learning techniques are particularly suitable for the finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong lensing systems on the basis of their morphological characteristics. We train and test our models on different subsamples of a data set of forty thousand mock images, having characteristics similar to those expected in the wide survey planned with the ESA mission \Euclid, gradually including larger fractions of faint lenses. We also evaluate the importance of adding information about the colour difference between the lens and source galaxies by repeating the same training on single-band and multi-band images. Our models find samples of clear lenses with $\gtrsim 90\%$ precision and completeness, without significant differences in the performance of the three architectures. Nevertheless, when including lenses with fainter arcs in the training set, the three models' performance deteriorates with accuracy values of $\sim 0.87$ to $\sim 0.75$ depending on the model. Our analysis confirms the potential of the application of CNNs to the identification of galaxy-scale strong lenses. We suggest that specific training with separate classes of lenses might be needed for detecting the faint lenses since the addition of the colour information does not yield a significant improvement in the current analysis, with the accuracy ranging from $\sim 0.89$ to $\sim 0.78$ for the different models.

arXiv comment23 pages,12 figures

References in corpus (81)

• The Structure of Cold Dark Matter Halos
• A systematic study of the class imbalance problem in convolutional neural networks
• The Hyper Suprime-Cam SSP Survey: Overview and Survey Design
• The Dark Energy Survey: more than dark energy - an overview
• The Sloan Lens ACS Survey. I. A Large Spectroscopically Selected Sample of Massive Early-Type Lens Galaxies
• The Sloan Lens ACS Survey. IV: the mass density profile of early-type galaxies out to 100 effective radii
• Galaxies in the Hubble Ultra Deep Field: I. Detection, Multiband Photometry, Photometric Redshifts, and Morphology
• Massive Dark-matter halos and Evolution of Early-type Galaxies to z=1
• The Detection of a Population of Submillimeter-Bright, Strongly-Lensed Galaxies
• The Cosmic Lens All-Sky Survey: I. Source selection and observations
• Improving galaxy morphologies for SDSS with Deep Learning
• The population of galaxy-galaxy strong lenses in forthcoming optical imaging surveys
• Weighing simulated galaxy clusters using lensing and X-ray
• The first and second data releases of the Kilo-Degree Survey
• A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning
• Two-dimensional kinematics of SLACS lenses: III. Mass structure and dynamics of early-type lens galaxies beyond z ~ 0.1
• Photometric redshifts from SDSS images using a Convolutional Neural Network
• The BOSS Emission-Line Lens Survey (BELLS). I. A large spectroscopically selected sample of Lens Galaxies at redshift ~ 0.5
• Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks
• The CFHTLS Strong Lensing Legacy Survey: I. Survey overview and T0002 release sample
• An excess of small-scale gravitational lenses observed in galaxy clusters
• Disentangling Baryons and Dark Matter in the Spiral Gravitational Lens B1933+503
• The CFHT Legacy Survey: stacked images and catalogs
• The Strong Gravitational Lens Finding Challenge
• Cosmological constraints with deep learning from KiDS-450 weak lensing maps
• The Herschel-ATLAS: a sample of 500μm-selected lensed galaxies over 600 square degrees
• Finding strong lenses in CFHTLS using convolutional neural networks
• Galaxy Morphology Classification with Deep Convolutional Neural Networks
• RingFinder: automated detection of galaxy-scale gravitational lenses in ground-based multi-filter imaging data
• The stellar and dark matter distributions in elliptical galaxies from the ensemble of strong gravitational lenses
• LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks
• Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). I. Automatic search for galaxy-scale strong lenses
• Finding high-redshift strong lenses in DES using convolutional neural networks
• Constraining sterile neutrino cosmologies with strong gravitational lensing observations at redshift z~0.2
• Transfer learning for galaxy morphology from one survey to another
• The SL2S Galaxy-scale Gravitational Lens Sample. I. The alignment of mass and light in massive early-type galaxies at z=0.2-0.9
• Euclid preparation. XVIII. The NISP photometric system
• Automated detection of galaxy-scale gravitational lenses in high resolution imaging data
• Arcfinder: An algorithm for the automatic detection of gravitational arcs
• HOLISMOKES -- II. Identifying galaxy-scale strong gravitational lenses in Pan-STARRS using convolutional neural networks
• New high-quality strong lens candidates with deep learning in the Kilo Degree Survey
• Realistic simulations of gravitational lensing by galaxy clusters: extracting arc parameters from mock DUNE images
• A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
• A gravitationally lensed water maser in the early Universe
• GLAMER Part I: A Code for Gravitational Lensing Simulations with Adaptive Mesh Refinement
• GLAMER Part II: Multiple Plane Gravitational Lensing
• High-quality strong lens candidates in the final Kilo Degree survey footprint
• Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks
• A new high-precision strong lensing model of the galaxy cluster MACS J0416.1-2403
• HOLISMOKES. VI. New galaxy-scale strong lens candidates from the HSC-SSP imaging survey
• Galaxy Morphology Network: A Convolutional Neural Network Used to Study Morphology and Quenching in $\sim 100,000$ SDSS and $\sim 20,000$ CANDELS Galaxies
• Gravitational lensing reveals extreme dust-obscured star formation in quasar host galaxies
• Limitations for shapelet-based weak-lensing measurements
• HOLISMOKES. VIII. High-redshift, strong-lens search in the Hyper Suprime-Cam Subaru Strategic Program
• On the average density profile of dark-matter halos in the inner regions of massive early-type galaxies
• Using convolutional neural networks to predict galaxy metallicity from three-color images
• On the dissection of degenerate cosmologies with machine learning
• Deep Learning for Strong Lensing Search: Tests of the Convolutional Neural Networks and New Candidates from KiDS DR3
• The probability of galaxy-galaxy strong lensing events in hydrodynamical simulations of galaxy clusters
• Illuminating the past 8 billion years of cold gas towards two gravitationally lensed quasars
• The inner dark matter distribution of the Cosmic Horseshoe (J1148+1930) with gravitational lensing and dynamics
• Strong lensing in UNIONS: Toward a pipeline from discovery to modeling
• Strong Gravitational Lensing with SKA
• Sensitivity of strong lensing observations to dark matter substructure: a case study with Euclid
• Euclid: Reconstruction of Weak Lensing mass maps for non-Gaussianity studies
• The search for galaxy cluster members with deep learning of panchromatic HST imaging and extensive spectroscopy
• Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). VIII. New galaxy-scale lenses from the HSC SSP
• Multi-color detection of gravitational arcs
• Strong lensing selection effects
• Reliable Shapelet Image Analysis
• Galaxy cluster cores as seen with VLT/MUSE: new strong-lensing analyses of RX J2129.4+0009, MS 0451.6-0305 & MACSJ2129.4-0741
• CLASH: Extending galaxy strong lensing to small physical scales with distant sources highly-magnified by galaxy cluster members
• Statistical strong lensing. III. Inferences with complete samples of lenses
• Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry
• Streamlined Lensed Quasar Identification in Multiband Images via Ensemble Networks
• Galaxy-galaxy lensing in the outskirts of CLASH clusters: constraints on local shear and testing mass-luminosity scaling relation
• Probing the Slope of Cluster Mass Profile with Gravitational Einstein Rings: Application to Abell 1689
• Constraining stellar population parameters from narrow band photometric surveys using convolutional neural networks
• PhotoWeb redshift: boosting photometric redshift accuracy with large spectroscopic surveys
• Discovery of two Einstein crosses from massive post--blue nugget galaxies at z>1 in KiDS
• Searching for galaxy-scale strong-lenses in galaxy clusters with deep networks -- I: methodology and network performance

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• Euclid. I. Overview of the Euclid mission
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• Euclid preparation. LI. Forecasting the recovery of galaxy physical properties and their relations with template-fitting and machine-learning methods
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• Galaxy-scale lens search in the PEARLS NEP TDF and CEERS JWST fields
• Gaia GraL: Gaia gravitational lens systems IX. Using XGBoost to explore the Gaia Focused Product Release GravLens catalogue
• Gravitational Lensing of Gravitational Waves from Astrophysical Sources: Theory, Detection, and Applications