Search of strong lens systems in the Dark Energy Survey using convolutional neural networks

Rojas, Karina; Savary, E.; Clément, B.; Maus, M.; Courbin, F.; Lemon, C.; Chan, J. H. H.; Vernardos, G.; Joseph, R.; Cañameras, R.; Galan, A.

doi:10.1051/0004-6361/202142119

Search of strong lens systems in the Dark Energy Survey using convolutional neural networks

Journal Article · Tue Dec 06 00:00:00 EST 2022 · Astronomy and Astrophysics

DOI:https://doi.org/10.1051/0004-6361/202142119· OSTI ID:2423872

^[1]; Savary, E. ^[1]; Clément, B. ^[1]; Maus, M. ^[1]; Courbin, F. ^[1]; Lemon, C. ^[1]; Chan, J. H. H. ^[1]; Vernardos, G. ^[1]; Joseph, R. ^[2]; Cañameras, R. ^[3]; Galan, A. ^[1]

Ecole Polytechnique Federale Lausanne (EPFL) (Switzerland)
Princeton Univ., NJ (United States)
Max Planck Institute for Astrophysics, Garching (Germany)

We present our search for strong lens, galaxy-scale systems in the first data release of the Dark Energy Survey (DES), based on a color-selected parent sample of 18 745 029 luminous red galaxies (LRGs). We used a convolutional neural network (CNN) to grade this LRG sample with values between 0 (non-lens) and 1 (lens). Our training set of mock lenses is data-driven, that is, it uses lensed sources taken from HST-COSMOS images and lensing galaxies from DES images of our LRG sample. A total of 76 582 cutouts were obtained with a score above 0.9, which were then visually inspected and classified into two catalogs. The first one contains 405 lens candidates, of which 90 present clear lensing features and counterparts, while the other 315 require more evidence, such as higher resolution imaging or spectra, to be conclusive. A total of 186 candidates are newly identified by our search, of which 16 are among the 90 most promising (best) candidates. The second catalog includes 539 ring galaxy candidates. This catalog will be a useful false positive sample for training future CNNs. For the 90 best lens candidates we carry out color-based deblending of the lens and source light without fitting any analytical profile to the data. This method is shown to be very efficient in the deblending, even for very compact objects and for objects with a complex morphology. Finally, from the 90 best lens candidates, we selected 52 systems with one single deflector to test an automated modeling pipeline that has the capacity to successfully model 79% of the sample within an acceptable computing runtime.

View Accepted Manuscript (DOE)

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

Sponsoring Organization:: National Science Foundation (NSF); USDOE

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 2423872

Journal Information:: Astronomy and Astrophysics, Journal Name: Astronomy and Astrophysics Vol. 668; ISSN 0004-6361

Publisher:: EDP SciencesCopyright Statement

Country of Publication:: United States

Language:: English

References (82)

GalSim: The modular galaxy image simulation toolkit Rowe, B. T. P.; Jarvis, M.; Mandelbaum, R. Astronomy and Computing, Vol. 10 https://doi.org/10.1016/j.ascom.2015.02.002	journal	April 2015
scarlet: Source separation in multi-band images by Constrained Matrix Factorization Melchior, P.; Moolekamp, F.; Jerdee, M. Astronomy and Computing, Vol. 24 https://doi.org/10.1016/j.ascom.2018.07.001	journal	July 2018
lenstronomy: Multi-purpose gravitational lens modelling software package Birrer, Simon; Amara, Adam Physics of the Dark Universe, Vol. 22 https://doi.org/10.1016/j.dark.2018.11.002	journal	December 2018
0957 + 561 A, B: twin quasistellar objects or gravitational lens? Walsh, D.; Carswell, R. F.; Weymann, R. J. Nature, Vol. 279, Issue 5712 https://doi.org/10.1038/279381a0	journal	May 1979
SciPy 1.0: fundamental algorithms for scientific computing in Python Virtanen, Pauli; Gommers, Ralf; Oliphant, Travis E. Nature Methods https://doi.org/10.1038/s41592-019-0686-2	journal	February 2020
Strong lensing constraints on the velocity dispersion and density profile of elliptical galaxies Davis, Adam N.; Huterer, Dragan; Krauss, Lawrence M. Monthly Notices of the Royal Astronomical Society, Vol. 344, Issue 4 https://doi.org/10.1046/j.1365-8711.2003.06789.x	journal	October 2003
A new window of exploration in the mass spectrum: strong lensing by galaxy groups in the SL2S Limousin, M.; Cabanac, R.; Gavazzi, R. Astronomy & Astrophysics, Vol. 502, Issue 2 https://doi.org/10.1051/0004-6361/200811473	journal	June 2009
Multi-colour detection of gravitational arcs Maturi, Matteo; Mizera, Sebastian; Seidel, Gregor Astronomy & Astrophysics, Vol. 567 https://doi.org/10.1051/0004-6361/201321634	journal	July 2014
A PCA-based automated finder for galaxy-scale strong lenses Joseph, R.; Courbin, F.; Metcalf, R. B. Astronomy & Astrophysics, Vol. 566 https://doi.org/10.1051/0004-6361/201423365	journal	June 2014
The VIMOS Ultra-Deep Survey: ~10 000 galaxies with spectroscopic redshifts to study galaxy assembly at early epochs 2 < z ≃ 6 Le Fèvre, O.; Tasca, L. A. M.; Cassata, P. Astronomy & Astrophysics, Vol. 576 https://doi.org/10.1051/0004-6361/201423829	journal	April 2015
The 0.1 < z < 1.65 evolution of the bright end of the [O ii] luminosity function Comparat, Johan; Richard, Johan; Kneib, Jean-Paul Astronomy & Astrophysics, Vol. 575 https://doi.org/10.1051/0004-6361/201424767	journal	February 2015
Multi-band morpho-Spectral Component Analysis Deblending Tool (MuSCADeT): Deblending colourful objects Joseph, R.; Courbin, F.; Starck, J. -L. Astronomy & Astrophysics, Vol. 589 https://doi.org/10.1051/0004-6361/201527923	journal	April 2016
The VIMOS Ultra Deep Survey first data release: Spectra and spectroscopic redshifts of 698 objects up to z _spec ~ 6 in CANDELS Tasca, L. A. M.; Le Fèvre, O.; Ribeiro, B. Astronomy & Astrophysics, Vol. 600 https://doi.org/10.1051/0004-6361/201527963	journal	April 2017
The PCA Lens-Finder: application to CFHTLS Paraficz, D.; Courbin, F.; Tramacere, A. Astronomy & Astrophysics, Vol. 592 https://doi.org/10.1051/0004-6361/201527971	journal	July 2016
TDCOSMO: I. An exploration of systematic uncertainties in the inference of H ₀ from time-delay cosmography Millon, M.; Galan, A.; Courbin, F. Astronomy & Astrophysics, Vol. 639 https://doi.org/10.1051/0004-6361/201937351	journal	July 2020
HOLISMOKES: II. Identifying galaxy-scale strong gravitational lenses in Pan-STARRS using convolutional neural networks Cañameras, R.; Schuldt, S.; Suyu, S. H. Astronomy & Astrophysics, Vol. 644 https://doi.org/10.1051/0004-6361/202038219	journal	December 2020
The CFHTLS strong lensing legacy survey: I. Survey overview and T0002 release sample Cabanac, R. A.; Alard, C.; Dantel-Fort, M. Astronomy & Astrophysics, Vol. 461, Issue 3 https://doi.org/10.1051/0004-6361:20065810	journal	October 2006
COSMOGRAIL: the COSmological MOnitoring of GRAvItational Lenses: V. The time delay in SDSS J1650+4251 Vuissoz, C.; Courbin, F.; Sluse, D. Astronomy & Astrophysics, Vol. 464, Issue 3 https://doi.org/10.1051/0004-6361:20065823	journal	January 2007
SExtractor: Software for source extraction Bertin, E.; Arnouts, S. Astronomy and Astrophysics Supplement Series, Vol. 117, Issue 2 https://doi.org/10.1051/aas:1996164	journal	June 1996
The VizieR database of astronomical catalogues Ochsenbein, F.; Bauer, P.; Marcout, J. Astronomy and Astrophysics Supplement Series, Vol. 143, Issue 1 https://doi.org/10.1051/aas:2000169	journal	April 2000
The SIMBAD astronomical database: The CDS reference database for astronomical objects Wenger, M.; Ochsenbein, F.; Egret, D. Astronomy and Astrophysics Supplement Series, Vol. 143, Issue 1 https://doi.org/10.1051/aas:2000332	journal	April 2000
The Multidimensional Prediction Problem Rosenblatt, M. Proceedings of the National Academy of Sciences, Vol. 43, Issue 11 https://doi.org/10.1073/pnas.43.11.989	journal	November 1957
The statistics of gravitational lenses - The distributions of image angular separations and lens redshifts Turner, E. L.; Ostriker, J. P.; Gott, J. R. , Iii The Astrophysical Journal, Vol. 284 https://doi.org/10.1086/162379	journal	September 1984
Shear and Ellipticity in Gravitational Lenses Keeton, C. R.; Kochanek, C. S.; Seljak, U. The Astrophysical Journal, Vol. 482, Issue 2 https://doi.org/10.1086/304172	journal	June 1997
An Estimate ofH₀from Keck Spectroscopy of the Gravitational Lens System 0957+561 Falco, Emilio E.; Shapiro, Irwin I.; Moustakas, Leonidas A. The Astrophysical Journal, Vol. 484, Issue 1 https://doi.org/10.1086/304339	journal	July 1997
Strong Gravitational Lensing Time Delay Statistics and the Density Profile of Dark Halos Oguri, Masamune; Taruya, Atsushi; Suto, Yasushi The Astrophysical Journal, Vol. 568, Issue 2 https://doi.org/10.1086/339064	journal	April 2002
COSMOS: Hubble Space Telescope Observations Scoville, N.; Abraham, R. G.; Aussel, H. The Astrophysical Journal Supplement Series, Vol. 172, Issue 1 https://doi.org/10.1086/516580	journal	September 2007
zCOSMOS: A Large VLT/VIMOS Redshift Survey Covering 0 < z < 3 in the COSMOS Field Lilly, S. J.; Fevre, O. Le; Renzini, A. The Astrophysical Journal Supplement Series, Vol. 172, Issue 1 https://doi.org/10.1086/516589	journal	September 2007
Weak Gravitational Lensing with COSMOS: Galaxy Selection and Shape Measurements Leauthaud, Alexie; Massey, Richard; Kneib, Jean‐Paul The Astrophysical Journal Supplement Series, Vol. 172, Issue 1 https://doi.org/10.1086/516598	journal	September 2007
The COSMOS Survey: Hubble Space Telescope Advanced Camera for Surveys Observations and Data Processing Koekemoer, A. M.; Aussel, H.; Calzetti, D. The Astrophysical Journal Supplement Series, Vol. 172, Issue 1 https://doi.org/10.1086/520086	journal	September 2007
`emcee` : The MCMC Hammer Foreman-Mackey, Daniel; Hogg, David W.; Lang, Dustin Publications of the Astronomical Society of the Pacific, Vol. 125, Issue 925 https://doi.org/10.1086/670067	journal	March 2013
The dark Energy Camera Flaugher, B.; Diehl, H. T.; Honscheid, K. The Astronomical Journal, Vol. 150, Issue 5 https://doi.org/10.1088/0004-6256/150/5/150	journal	October 2015
The Sloan lens acs Survey. ix. Colors, Lensing, and Stellar Masses of Early-Type Galaxies Auger, M. W.; Treu, T.; Bolton, A. S. The Astrophysical Journal, Vol. 705, Issue 2 https://doi.org/10.1088/0004-637X/705/2/1099	journal	October 2009
The Cfhtls-Strong Lensing Legacy Survey (Sl2s): Investigating the Group-Scale Lenses with the Sarcs Sample More, A.; Cabanac, R.; More, S. The Astrophysical Journal, Vol. 749, Issue 1 https://doi.org/10.1088/0004-637X/749/1/38	journal	March 2012
Ringfinder: Automated Detection of Galaxy-Scale Gravitational Lenses in Ground-Based Multi-Filter Imaging data Gavazzi, Raphaël; Marshall, Philip J.; Treu, Tommaso The Astrophysical Journal, Vol. 785, Issue 2 https://doi.org/10.1088/0004-637X/785/2/144	journal	April 2014
Cosmology with Strong-Lensing Systems Cao, Shuo; Biesiada, Marek; Gavazzi, Raphaël The Astrophysical Journal, Vol. 806, Issue 2 https://doi.org/10.1088/0004-637X/806/2/185	journal	June 2015
Probing the dark Matter Radial Profile in lens Galaxies and the size of X-Ray Emitting Region in Quasars with Microlensing Jiménez-Vicente, J.; Mediavilla, E.; Kochanek, C. S. The Astrophysical Journal, Vol. 806, Issue 2 https://doi.org/10.1088/0004-637X/806/2/251	journal	June 2015
Chitah: STRONG-GRAVITATIONAL-LENS HUNTER IN IMAGING SURVEYS Chan, James H. H.; Suyu, Sherry H.; Chiueh, Tzihong The Astrophysical Journal, Vol. 807, Issue 2 https://doi.org/10.1088/0004-637X/807/2/138	journal	July 2015
The Population of Galaxy–Galaxy Strong Lenses in Forthcoming Optical Imaging Surveys Collett, Thomas E. The Astrophysical Journal, Vol. 811, Issue 1 https://doi.org/10.1088/0004-637X/811/1/20	journal	September 2015
Gravitational lens Modeling with Basis sets Birrer, Simon; Amara, Adam; Refregier, Alexandre The Astrophysical Journal, Vol. 813, Issue 2 https://doi.org/10.1088/0004-637X/813/2/102	journal	November 2015
The Advanced Camera for Surveys General Catalog: Structural Parameters for Approximately half a Million Galaxies Griffith, Roger L.; Cooper, Michael C.; Newman, Jeffrey A. The Astrophysical Journal Supplement Series, Vol. 200, Issue 1 https://doi.org/10.1088/0067-0049/200/1/9	journal	May 2012
THE FMOS-COSMOS SURVEY OF STAR-FORMING GALAXIES AT z ∼ 1.6. III. SURVEY DESIGN, PERFORMANCE, AND SAMPLE CHARACTERISTICS Silverman, J. D.; Kashino, D.; Sanders, D. The Astrophysical Journal Supplement Series, Vol. 220, Issue 1 https://doi.org/10.1088/0067-0049/220/1/12	journal	September 2015
Constraints on cosmological models from strong gravitational lensing systems Cao, Shuo; Pan, Yu; Biesiada, Marek Journal of Cosmology and Astroparticle Physics, Vol. 2012, Issue 03 https://doi.org/10.1088/1475-7516/2012/03/016	journal	March 2012
Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI) – V. Group-to-cluster scale lens search from the HSC–SSP Survey Jaelani, Anton T.; More, Anupreeta; Oguri, Masamune Monthly Notices of the Royal Astronomical Society, Vol. 495, Issue 1 https://doi.org/10.1093/mnras/staa1062	journal	April 2020
Deep learning for strong lensing search: tests of the convolutional neural networks and new candidates from KiDS DR3 He, Zizhao; Er, Xinzhong; Long, Qian Monthly Notices of the Royal Astronomical Society, Vol. 497, Issue 1 https://doi.org/10.1093/mnras/staa1917	journal	July 2020
Observation and confirmation of nine strong-lensing systems in Dark Energy Survey Year 1 data Nord, B.; Buckley-Geer, E.; Lin, H. Monthly Notices of the Royal Astronomical Society, Vol. 494, Issue 1 https://doi.org/10.1093/mnras/staa200	journal	January 2020
The STRong lensing Insights into the Dark Energy Survey (STRIDES) 2017/2018 follow-up campaign: discovery of 10 lensed quasars and 10 quasar pairs Lemon, C.; Auger, M. W.; McMahon, R. Monthly Notices of the Royal Astronomical Society, Vol. 494, Issue 3 https://doi.org/10.1093/mnras/staa652	journal	March 2020
Lenses In VoicE (LIVE): searching for strong gravitational lenses in the VOICE@VST survey using convolutional neural networks Gentile, Fabrizio; Tortora, Crescenzo; Covone, Giovanni Monthly Notices of the Royal Astronomical Society, Vol. 510, Issue 1 https://doi.org/10.1093/mnras/stab3386	journal	November 2021
Dark matter haloes of massive elliptical galaxies at z ∼ 0.2 are well described by the Navarro–Frenk–White profile Shajib, Anowar J.; Treu, Tommaso; Birrer, Simon Monthly Notices of the Royal Astronomical Society, Vol. 503, Issue 2 https://doi.org/10.1093/mnras/stab536	journal	February 2021
Cosmological constraints from the double source plane lens SDSSJ0946+1006 Collett, Thomas E.; Auger, Matthew W. Monthly Notices of the Royal Astronomical Society, Vol. 443, Issue 2 https://doi.org/10.1093/mnras/stu1190	journal	July 2014
The SDSS-III BOSS quasar lens survey: discovery of 13 gravitationally lensed quasars More, Anupreeta; Oguri, Masamune; Kayo, Issha Monthly Notices of the Royal Astronomical Society, Vol. 456, Issue 2 https://doi.org/10.1093/mnras/stv2813	journal	December 2015
Galaxy Zoo: morphological classifications for 120 000 galaxies in HST legacy imaging Willett, Kyle W.; Galloway, Melanie A.; Bamford, Steven P. Monthly Notices of the Royal Astronomical Society, Vol. 464, Issue 4 https://doi.org/10.1093/mnras/stw2568	journal	October 2016
H0LiCOW – V. New COSMOGRAIL time delays of HE 0435−1223: H ₀ to 3.8 per cent precision from strong lensing in a flat ΛCDM model Bonvin, V.; Courbin, F.; Suyu, S. H. Monthly Notices of the Royal Astronomical Society, Vol. 465, Issue 4 https://doi.org/10.1093/mnras/stw3006	journal	November 2016
Finding strong lenses in CFHTLS using convolutional neural networks Jacobs, C.; Glazebrook, K.; Collett, T. Monthly Notices of the Royal Astronomical Society, Vol. 471, Issue 1 https://doi.org/10.1093/mnras/stx1492	journal	June 2017
CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding Lanusse, François; Ma, Quanbin; Li, Nan Monthly Notices of the Royal Astronomical Society, Vol. 473, Issue 3 https://doi.org/10.1093/mnras/stx1665	journal	July 2017
Finding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks Petrillo, C. E.; Tortora, C.; Chatterjee, S. Monthly Notices of the Royal Astronomical Society, Vol. 472, Issue 1 https://doi.org/10.1093/mnras/stx2052	journal	August 2017
AutoLens: automated modeling of a strong lens’s light, mass, and source Nightingale, J. W.; Dye, S.; Massey, Richard J. Monthly Notices of the Royal Astronomical Society, Vol. 478, Issue 4 https://doi.org/10.1093/mnras/sty1264	journal	May 2018
LinKS: discovering galaxy-scale strong lenses in the Kilo-Degree Survey using convolutional neural networks Petrillo, C. E.; Tortora, C.; Vernardos, G. Monthly Notices of the Royal Astronomical Society, Vol. 484, Issue 3 https://doi.org/10.1093/mnras/stz189	journal	January 2019
Finding high-redshift strong lenses in DES using convolutional neural networks Jacobs, C.; Collett, T.; Glazebrook, K. Monthly Notices of the Royal Astronomical Society, Vol. 484, Issue 4 https://doi.org/10.1093/mnras/stz272	journal	January 2019
H0LiCOW – XIII. A 2.4 per cent measurement of H0 from lensed quasars: 5.3σ tension between early- and late-Universe probes Wong, Kenneth C.; Suyu, Sherry H.; Chen, Geoff C-F Monthly Notices of the Royal Astronomical Society, Vol. 498, Issue 1 https://doi.org/10.1093/mnras/stz3094	journal	September 2019
The Canarias Einstein ring: a newly discovered optical Einstein ring Bettinelli, M.; Simioni, M.; Aparicio, A. Monthly Notices of the Royal Astronomical Society: Letters, Vol. 461, Issue 1 https://doi.org/10.1093/mnrasl/slw097	journal	May 2016
Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). I. Automatic search for galaxy-scale strong lenses Sonnenfeld, Alessandro; Chan, James H. H.; Shu, Yiping Publications of the Astronomical Society of Japan, Vol. 70, Issue SP1 https://doi.org/10.1093/pasj/psx062	journal	September 2017
The Hyper Suprime-Cam SSP Survey: Overview and survey design Aihara, Hiroaki; Arimoto, Nobuo; Armstrong, Robert Publications of the Astronomical Society of Japan, Vol. 70, Issue SP1 https://doi.org/10.1093/pasj/psx066	journal	September 2017
Particle swarm optimization Kennedy, J.; Eberhart, R. Proceedings of ICNN'95 - International Conference on Neural Networks https://doi.org/10.1109/ICNN.1995.488968	conference	January 1995
Matplotlib: A 2D Graphics Environment Hunter, John D. Computing in Science & Engineering, Vol. 9, Issue 3 https://doi.org/10.1109/MCSE.2007.55	journal	January 2007
The NumPy Array: A Structure for Efficient Numerical Computation van der Walt, Stéfan; Colbert, S. Chris; Varoquaux, Gaël Computing in Science & Engineering, Vol. 13, Issue 2 https://doi.org/10.1109/MCSE.2011.37	journal	March 2011
The Undecimated Wavelet Decomposition and its Reconstruction Starck, Jean-Luc; Fadili, Jalal; Murtagh, Fionn IEEE Transactions on Image Processing, Vol. 16, Issue 2 https://doi.org/10.1109/TIP.2006.887733	journal	February 2007
Galaxy And Mass Assembly (GAMA): Structural Investigation of Galaxies via Model Analysis Kelvin, Lee S.; Driver, Simon P.; Robotham, Aaron S. G. Monthly Notices of the Royal Astronomical Society, Vol. 421, Issue 2 https://doi.org/10.1111/j.1365-2966.2012.20355.x	journal	March 2012
Backpropagation Applied to Handwritten Zip Code Recognition LeCun, Y.; Boser, B.; Denker, J. S. Neural Computation, Vol. 1, Issue 4 https://doi.org/10.1162/neco.1989.1.4.541	journal	December 1989
THE COSMOS2015 CATALOG: EXPLORING THE 1 < z < 6 UNIVERSE WITH HALF A MILLION GALAXIES Laigle, C.; McCracken, H. J.; Ilbert, O. The Astrophysical Journal Supplement Series, Vol. 224, Issue 2 https://doi.org/10.3847/0067-0049/224/2/24	journal	June 2016
Binary Companions of Evolved Stars in APOGEE DR14: Search Method and Catalog of ∼5000 Companions Price-Whelan, Adrian M.; Hogg, David W.; Rix, Hans-Walter The Astronomical Journal, Vol. 156, Issue 1 https://doi.org/10.3847/1538-3881/aac387	journal	June 2018
The DEIMOS 10K Spectroscopic Survey Catalog of the COSMOS Field Hasinger, G.; Capak, P.; Salvato, M. The Astrophysical Journal, Vol. 858, Issue 2 https://doi.org/10.3847/1538-4357/aabacf	journal	May 2018
Survey of Gravitationally Lensed Objects in HSC Imaging (SuGOHI). II. Environments and Line-of-Sight Structure of Strong Gravitational Lens Galaxies to z ∼ 0.8 Wong, Kenneth C.; Sonnenfeld, Alessandro; Chan, James H. H. The Astrophysical Journal, Vol. 867, Issue 2 https://doi.org/10.3847/1538-4357/aae381	journal	November 2018
LSST: From Science Drivers to Reference Design and Anticipated Data Products Ivezić, Željko; Kahn, Steven M.; Tyson, J. Anthony The Astrophysical Journal, Vol. 873, Issue 2 https://doi.org/10.3847/1538-4357/ab042c	journal	March 2019
Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique Avestruz, Camille; Li, Nan; Zhu, Hanjue The Astrophysical Journal, Vol. 877, Issue 1 https://doi.org/10.3847/1538-4357/ab16d9	journal	May 2019
Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey Huang, X.; Storfer, C.; Ravi, V. The Astrophysical Journal, Vol. 894, Issue 1 https://doi.org/10.3847/1538-4357/ab7ffb	journal	May 2020
New High-quality Strong Lens Candidates with Deep Learning in the Kilo-Degree Survey Li, R.; Napolitano, N. R.; Tortora, C. The Astrophysical Journal, Vol. 899, Issue 1 https://doi.org/10.3847/1538-4357/ab9dfa	journal	August 2020
Discovering New Strong Gravitational Lenses in the DESI Legacy Imaging Surveys Huang, X.; Storfer, C.; Gu, A. The Astrophysical Journal, Vol. 909, Issue 1 https://doi.org/10.3847/1538-4357/abd62b	journal	March 2021
Gravitational Lensing Formalism in a Curved Arc Basis: A Continuous Description of Observables and Degeneracies from the Weak to the Strong Lensing Regime Birrer, Simon The Astrophysical Journal, Vol. 919, Issue 1 https://doi.org/10.3847/1538-4357/ac1108	journal	September 2021
The DES Bright Arcs Survey: Hundreds of Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey Science Verification and Year 1 Observations Diehl, H. T.; Buckley-Geer, E. J.; Lindgren, K. A. The Astrophysical Journal Supplement Series, Vol. 232, Issue 1 https://doi.org/10.3847/1538-4365/aa8667	journal	September 2017
The Dark Energy Survey: Data Release 1 Abbott, T. M. C.; Abdalla, F. B.; Allam, S. The Astrophysical Journal Supplement Series, Vol. 239, Issue 2 https://doi.org/10.3847/1538-4365/aae9f0	journal	November 2018
An Extended Catalog of Galaxy–Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks Jacobs, C.; Collett, T.; Glazebrook, K. The Astrophysical Journal Supplement Series, Vol. 243, Issue 1 https://doi.org/10.3847/1538-4365/ab26b6	journal	July 2019

Similar Records

Survey of gravitationally lensed objects in HSC imaging (SuGOHI) – X. Strong lens finding in the HSC-SSP using convolutional neural networks

Journal Article · Thu Oct 24 20:00:00 EDT 2024 · Monthly Notices of the Royal Astronomical Society · OSTI ID:2477070

Discovering Strong Gravitational Lenses in the Dark Energy Survey with Interactive Machine Learning and Crowd-sourced Inspection with Space Warps

Journal Article · Sat Jan 25 23:00:00 EST 2025 · TBD · OSTI ID:2520109

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

Journal Article · Thu Jul 18 20:00:00 EDT 2019 · Astrophys.J.Suppl. · OSTI ID:1556970

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS
Astronomy & Astrophysics
catalogs
gravitational lensing: strong
surveys
techniques: image processing

Search of strong lens systems in the Dark Energy Survey using convolutional neural networks

Citation Formats

References (82)

Linked Research (1)

Similar Records

Related Subjects