Multi-colour detection of gravitational arcs
A&A, 567 (2014) A111
Published online: 2014-07-23
Articles citing this article
The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
Cited article:
This article has been cited by the following article(s):
Searching for Strong Gravitational Lenses
Cameron Lemon, Frédéric Courbin, Anupreeta More, Paul Schechter, Raoul Cañameras, Ludovic Delchambre, Calvin Leung, Yiping Shu, Chiara Spiniello, Yashar Hezaveh, Jonas Klüter and Richard McMahonSpace Science Reviews 220 (2) (2024)
https://doi.org/10.1007/s11214-024-01042-9
Euclid preparation
L. Leuzzi, M. Meneghetti, G. Angora, R. B. Metcalf, L. Moscardini, P. Rosati, P. Bergamini, F. Calura, B. Clément, R. Gavazzi, F. Gentile, M. Lochner, C. Grillo, G. Vernardos, N. Aghanim, A. Amara, L. Amendola, N. Auricchio, C. Bodendorf, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, et al.Astronomy & Astrophysics 681 A68 (2024)
https://doi.org/10.1051/0004-6361/202347244
HOLISMOKES
S. Schuldt, R. Cañameras, Y. Shu, et al.Astronomy & Astrophysics 671 A147 (2023)
https://doi.org/10.1051/0004-6361/202244325
Developing a victorious strategy to the second strong gravitational lensing data challenge
C R Bom, B M O Fraga, L O Dias, et al.Monthly Notices of the Royal Astronomical Society 515 (4) 5121 (2022)
https://doi.org/10.1093/mnras/stac2047
Search of strong lens systems in the Dark Energy Survey using convolutional neural networks
K. Rojas, E. Savary, B. Clément, et al.Astronomy & Astrophysics 668 A73 (2022)
https://doi.org/10.1051/0004-6361/202142119
The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: a catalogue of strong galaxy–galaxy lens candidates
Michael S Talbot, Joel R Brownstein, Kyle S Dawson, Jean-Paul Kneib and Julian BautistaMonthly Notices of the Royal Astronomical Society 502 (3) 4617 (2021)
https://doi.org/10.1093/mnras/stab267
Introduction to Gravitational Lensing
Massimo MeneghettiLecture Notes in Physics, Introduction to Gravitational Lensing 956 255 (2021)
https://doi.org/10.1007/978-3-030-73582-1_6
HOLISMOKES
S. Schuldt, S. H. Suyu, T. Meinhardt, et al.Astronomy & Astrophysics 646 A126 (2021)
https://doi.org/10.1051/0004-6361/202039574
Detecting strongly lensed supernovae at z ∼ 5–7 with LSST
Claes-Erik Rydberg, Daniel J Whalen, Matteo Maturi, et al.Monthly Notices of the Royal Astronomical Society 491 (2) 2447 (2020)
https://doi.org/10.1093/mnras/stz3203
The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples
James Pearson, Nan Li and Simon DyeMonthly Notices of the Royal Astronomical Society 488 (1) 991 (2019)
https://doi.org/10.1093/mnras/stz1750
Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique
Camille Avestruz, Nan Li, Hanjue 涵珏 Zhu 朱, Matthew Lightman, Thomas E. Collett and Wentao LuoThe Astrophysical Journal 877 (1) 58 (2019)
https://doi.org/10.3847/1538-4357/ab16d9
EasyCritics – I. Efficient detection of strongly lensing galaxy groups and clusters in wide-field surveys
Sebastian Stapelberg, Mauricio Carrasco and Matteo MaturiMonthly Notices of the Royal Astronomical Society 482 (2) 1824 (2019)
https://doi.org/10.1093/mnras/sty2784
LinKS: discovering galaxy-scale strong lenses in the Kilo-Degree Survey using convolutional neural networks
C E Petrillo, C Tortora, G Vernardos, et al.Monthly Notices of the Royal Astronomical Society 484 (3) 3879 (2019)
https://doi.org/10.1093/mnras/stz189
Models of gravitational lens candidates from Space Warps CFHTLS
Rafael Küng, Prasenjit Saha, Ignacio Ferreras, et al.Monthly Notices of the Royal Astronomical Society 474 (3) 3700 (2018)
https://doi.org/10.1093/mnras/stx3012
SpaghettiLens: A software stack for modeling gravitational lenses by citizen scientists
R. KüngAstronomy and Computing 23 115 (2018)
https://doi.org/10.1016/j.ascom.2018.02.007
Auto-detection of strong gravitational lenses using convolutional neural networks
James Pearson, Clara Pennock and Tom RobinsonEmergent Scientist 2 1 (2018)
https://doi.org/10.1051/emsci/2017010
A neural network gravitational arc finder based on the Mediatrix filamentation method
C. R. Bom, M. Makler, M. P. Albuquerque and C. H. BrandtAstronomy & Astrophysics 597 A135 (2017)
https://doi.org/10.1051/0004-6361/201629159
Finding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks
C. E. Petrillo, C. Tortora, S. Chatterjee, et al.Monthly Notices of the Royal Astronomical Society 472 (1) 1129 (2017)
https://doi.org/10.1093/mnras/stx2052
The PCA Lens-Finder: application to CFHTLS
D. Paraficz, F. Courbin, A. Tramacere, et al.Astronomy & Astrophysics 592 A75 (2016)
https://doi.org/10.1051/0004-6361/201527971
Space Warps– II. New gravitational lens candidates from the CFHTLS discovered through citizen science
Anupreeta More, Aprajita Verma, Philip J. Marshall, et al.Monthly Notices of the Royal Astronomical Society 455 (2) 1191 (2016)
https://doi.org/10.1093/mnras/stv1965
Characterizing SL2S galaxy groups using the Einstein radius
T. Verdugo, V. Motta, G. Foëx, et al.Astronomy & Astrophysics 571 A65 (2014)
https://doi.org/10.1051/0004-6361/201423696