Finding high-redshift strong lenses in DES using convolutional neural networks

Abstract

We search Dark Energy Survey (DES) Year 3 imaging data for galaxy-galaxy strong gravitational lenses using convolutional neural networks. We generate 250,000 simulated lenses at redshifts > 0.8 from which we create a data set for training the neural networks with realistic seeing, sky and shot noise. Using the simulations as a guide, we build a catalogue of 1.1 million DES sources with 1.8 < g - i < 5, 0.6 < g -r < 3, r_mag > 19, g_mag > 20 and i_mag > 18.2. We train two ensembles of neural networks on training sets consisting of simulated lenses, simulated non-lenses, and real sources. We use the neural networks to score images of each of the sources in our catalogue with a value from 0 to 1, and select those with scores greater than a chosen threshold for visual inspection, resulting in a candidate set of 7,301 galaxies. During visual inspection we rate 84 as "probably" or "definitely" lenses. Four of these are previously known lenses or lens candidates. We inspect a further 9,428 candidates with a different score threshold, and identify four new candidates. We present 84 new strong lens candidates, selected after a few hours of visual inspection by astronomers. This catalogue contains a comparable number of high-redshift lenses to that predicted by simulations. Based on simulations we estimate our sample to contain most discoverable lenses in this imaging and at this redshift range.

arXiv commentAccepted for publication in MNRAS

References in corpus (51)

• Deep Learning in Neural Networks: An Overview
• Improving neural networks by preventing co-adaptation of feature detectors
• COSMOS Photometric Redshifts with 30-bands for 2-deg2
• The Atlas3D project -- I. A volume-limited sample of 260 nearby early-type galaxies: science goals and selection criteria
• The Dark Energy Camera
• The SAURON project. II. Sample and early results
• The Sloan Lens ACS Survey. I. A Large Spectroscopically Selected Sample of Massive Early-Type Lens Galaxies
• The Dark Energy Survey Data Release 1
• H0LiCOW V. New COSMOGRAIL time delays of HE0435-1223: $H_0$ to 3.8% precision from strong lensing in a flat $\Lambda$CDM model
• Strong Lensing by Galaxies
• Massive Dark-matter halos and Evolution of Early-type Galaxies to z=1
• Gravitational detection of a low-mass dark satellite at cosmological distance
• The population of galaxy-galaxy strong lenses in forthcoming optical imaging surveys
• A highly magnified candidate for a young galaxy seen when the Universe was 500 Myrs old
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• Two-dimensional kinematics of SLACS lenses: III. Mass structure and dynamics of early-type lens galaxies beyond z ~ 0.1
• Internal and Collective Properties of Galaxies in the Sloan Digital Sky Survey
• The Dark Energy Survey Image Processing Pipeline
• The SL2S Galaxy-scale Lens Sample. IV. The dependence of the total mass density profile of early-type galaxies on redshift, stellar mass, and size
• CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding
• Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks
• The formation and assembly of a typical star-forming galaxy at z~3
• The CFHTLS-Strong Lensing Legacy Survey (SL2S): Investigating the group-scale lenses with the SARCS sample
• The luminosity and stellar mass Fundamental Plane of early-type galaxies
• The Strong Gravitational Lens Finding Challenge
• The Ultraviolet Spectrum of the Gravitationally Lensed Galaxy `The Cosmic Horseshoe': A Close-up of a Star-forming Galaxy at z = 2
• Finding strong lenses in CFHTLS using convolutional neural networks
• The SL2S Galaxy-scale Lens Sample. II. Cosmic evolution of dark and luminous mass in early-type galaxies
• RingFinder: automated detection of galaxy-scale gravitational lenses in ground-based multi-filter imaging data
• Modelling the line-of-sight contribution in substructure lensing
• The Co-Evolution of Total Density Profiles and Central Dark Matter Fractions in Simulated Early-Type Galaxies
• Space Warps: I. Crowd-sourcing the Discovery of Gravitational Lenses
• Space Warps II. New Gravitational Lens Candidates from the CFHTLS Discovered through Citizen Science
• The shape of the dark matter halo in the early-type galaxy NGC 2974
• Automated detection of galaxy-scale gravitational lenses in high resolution imaging data
• Arcfinder: An algorithm for the automatic detection of gravitational arcs
• Computational Methods for Gravitational Lensing
• Data Mining for Gravitationally Lensed Quasars
• The Sloan Lens ACS Survey. XI. Beyond Hubble resolution: size, luminosity and stellar mass of compact lensed galaxies at intermediate redshift
• PEGASE.2, a metallicity-consistent spectral evolution model of galaxies: the documentation and the code
• A Systematic Search for High Surface Brightness Giant Arcs in a Sloan Digital Sky Survey Cluster Sample
• Automatic detection of arcs and arclets formed by gravitational lensing
• The SLUGGS Survey: A comparison of total-mass profiles of early-type galaxies from observations and cosmological simulations, to $\sim$4 effective radii
• Euclid Mission: building of a Reference Survey
• Galaxy structure from multiple tracers: III. Radial variations in M87's IMF
• Observation and Confirmation of Six Strong Lensing Systems in The Dark Energy Survey Science Verification Data
• Thirty-fold: Extreme gravitational lensing of a quiescent galaxy at $z=1.6$
• Automated detection of gravitational arcs
• Revisiting the bulge-halo conspiracy II: Towards explaining its puzzling dependence on redshift
• The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification
• Improved mass constraints for two nearby strong-lensing elliptical galaxies from Hubble Space Telescope Imaging

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• The Atacama Cosmology Telescope: A Catalog of > 4000 Sunyaev-Zel'dovich Galaxy Clusters
• Strong gravitational lensing of explosive transients
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• The DECam Local Volume Exploration Survey: Overview and First Data Release
• Deep learning predictions of galaxy merger stage and the importance of observational realism
• LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks
• Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey
• Discovering New Strong Gravitational Lenses in the DESI Legacy Imaging Surveys
• OzDES multi-object fibre spectroscopy for the Dark Energy Survey: Results and second data release
• HOLISMOKES -- II. Identifying galaxy-scale strong gravitational lenses in Pan-STARRS using convolutional neural networks
• HOLISMOKES -- I. Highly Optimised Lensing Investigations of Supernovae, Microlensing Objects, and Kinematics of Ellipticals and Spirals
• Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks
• Mining for Strong Gravitational Lenses with Self-supervised Learning
• HOLISMOKES. VIII. High-redshift, strong-lens search in the Hyper Suprime-Cam Subaru Strategic Program
• KiDS-SQuaD II: Machine learning selection of bright extragalactic objects to search for new gravitationally lensed quasars
• On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars
• Project Dinos I: A joint lensing-dynamics constraint on the deviation from the power law in the mass profile of massive ellipticals
• Deep Learning for Strong Lensing Search: Tests of the Convolutional Neural Networks and New Candidates from KiDS DR3
• Strong lensing in UNIONS: Toward a pipeline from discovery to modeling
• GIGA-Lens: Fast Bayesian Inference for Strong Gravitational Lens Modeling
• Strong Lensing by Galaxies
• Detecting gravitational lenses using machine learning: exploring interpretability and sensitivity to rare lensing configurations
• Predictions for Strong Lens Detections with the Nancy Grace Roman Space Telescope
• New Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9
• A machine learning based approach to gravitational lens identification with the International LOFAR Telescope
• Detecting strongly lensed supernovae at z ~ 5-7 with LSST
• Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
• The impact of human expert visual inspection on the discovery of strong gravitational lenses
• Machine Learning Classifiers for Intermediate Redshift Emission Line Galaxies
• Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
• On the detectability of strong lensing in near-infrared surveys
• Deep Learning Assessment of galaxy morphology in S-PLUS DataRelease 1
• Euclid Preparation XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong lensing events
• Machine Learning and Cosmology
• Comparison of Multi-Class and Binary Classification Machine Learning Models in Identifying Strong Gravitational Lenses
• A Glimpse of the Stellar Populations and Elemental Abundances of Gravitationally Lensed, Quiescent Galaxies at $z\gtrsim 1$ with Keck Deep Spectroscopy
• LeMoN: Lens Modelling with Neural networks -- I. Automated modelling of strong gravitational lenses with Bayesian Neural Networks
• The DES Bright Arcs Survey: Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey 5,000 Sq. Deg. Footprint
• An extended catalog of galaxy-galaxy strong gravitational lenses discovered in DES using convolutional neural networks
• HOLISMOKES -- XI. Evaluation of supervised neural networks for strong-lens searches in ground-based imaging surveys
• The COSMOS-Web Lens Survey (COWLS) I: Discovery of >100 high redshift strong lenses in contiguous JWST imaging
• A model for galaxy-galaxy strong lensing statistics in surveys
• A Bayesian Approach to Strong Lens Finding in the Era of Wide-area Surveys
• Candidate high-redshift protoclusters and lensed galaxies in the Planck list of high-z sources overlapping with Herschel-SPIRE imaging
• Deep Learning in Wide-field Surveys: Fast Analysis of Strong Lenses in Ground-based Cosmic Experiments
• An astronomical image content-based recommendation system using combined deep learning models in a fully unsupervised mode
• Developing a Victorious Strategy to the Second Strong Gravitational Lensing Data Challenge
• Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02<z<0.1
• Machine learning technique for morphological classification of galaxies from the SDSS. III. Image-based inference of detailed features
• Searching for galaxy-scale strong-lenses in galaxy clusters with deep networks -- I: methodology and network performance
• Automatic identification of outliers in Hubble Space Telescope galaxy images
• A search for gravitationally lensed supernovae within the Zwicky Transient Facility public survey
• Constraining Cosmology with Double-source-plane Strong Gravitational Lenses from the AGEL Survey
• Searching for strong lensing by late-type galaxies in UNIONS
• Galaxies OBserved as Low-luminosity Identified Nebulae (GOBLIN): a catalog of 43,000 high-probability dwarf galaxy candidates in the UNIONS survey
• 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
• LenNet: Direct Detection and Localization of Strong Gravitational Lenses in Wide-Field Sky Survey Images
• Lenses In VoicE (LIVE): Searching for strong gravitational lenses in the VOICE@VST survey using Convolutional Neural Networks
• DeepMerge: Classifying High-redshift Merging Galaxies with Deep Neural Networks
• The AGEL Survey: Spectroscopic Confirmation of Strong Gravitational Lenses in the DES and DECaLS Fields Selected Using Convolutional Neural Networks
• Most Strong Lensing Deflectors in the AGEL Survey Are in Group and Cluster Environments
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• Extragalactic astrophysics with next-generation CMB experiments