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Efficient image denoising technique using the meshless method: Investigation of operator splitting RBF collocation method for two anisotropic diffusion-based PDEs
On building a cluster watchlist for identifying strongly lensed supernovae, gravitational waves and kilonovae
Mathilde Jauzac, Andrew Robertson, Richard Massey, et al. Monthly Notices of the Royal Astronomical Society 495(2) 1666 (2020) https://doi.org/10.1093/mnras/staa1274
Finding high-redshift strong lenses in DES using convolutional neural networks
C Jacobs, T Collett, K Glazebrook, et al. Monthly Notices of the Royal Astronomical Society 484(4) 5330 (2019) https://doi.org/10.1093/mnras/stz272
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 Luo The Astrophysical Journal 877(1) 58 (2019) https://doi.org/10.3847/1538-4357/ab16d9
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
Machine learning and Kolmogorov analysis to reveal gravitational lenses
S S Mirzoyan, H Khachatryan, G Yegorian and V G Gurzadyan Monthly Notices of the Royal Astronomical Society: Letters 489(1) L32 (2019) https://doi.org/10.1093/mnrasl/slz125
Using convolutional neural networks to identify gravitational lenses in astronomical images
Andrew Davies, Stephen Serjeant and Jane M Bromley Monthly Notices of the Royal Astronomical Society 487(4) 5263 (2019) https://doi.org/10.1093/mnras/stz1288
Using deep Residual Networks to search for galaxy-Ly α emitter lens candidates based on spectroscopic selection
EasyCritics – I. Efficient detection of strongly lensing galaxy groups and clusters in wide-field surveys
Sebastian Stapelberg, Mauricio Carrasco and Matteo Maturi Monthly Notices of the Royal Astronomical Society 482(2) 1824 (2019) https://doi.org/10.1093/mnras/sty2784
Deep convolutional neural networks as strong gravitational lens detectors
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
A neural network gravitational arc finder based on the Mediatrix filamentation method
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
THE DETECTION AND STATISTICS OF GIANT ARCS BEHIND CLASH CLUSTERS
Bingxiao Xu, Marc Postman, Massimo Meneghetti, Stella Seitz, Adi Zitrin, Julian Merten, Dani Maoz, Brenda Frye, Keiichi Umetsu, Wei Zheng, Larry Bradley, Jesus Vega and Anton Koekemoer The Astrophysical Journal 817(2) 85 (2016) https://doi.org/10.3847/0004-637X/817/2/85
OBSERVATION AND CONFIRMATION OF SIX STRONG-LENSING SYSTEMS IN THE DARK ENERGY SURVEY SCIENCE VERIFICATION DATA*
B. Nord, E. Buckley-Geer, H. Lin, H. T. Diehl, J. Helsby, N. Kuropatkin, A. Amara, T. Collett, S. Allam, G. B. Caminha, C. De Bom, S. Desai, H. Dúmet-Montoya, M. Elidaiana da S. Pereira, D. A. Finley, B. Flaugher, C. Furlanetto, H. Gaitsch, M. Gill, K. W. Merritt, A. More, D. Tucker, A. Saro, E. S. Rykoff, E. Rozo, et al. The Astrophysical Journal 827(1) 51 (2016) https://doi.org/10.3847/0004-637X/827/1/51
A simple prescription for simulating and characterizing gravitational arcs
Relativistic Astrophysics Legacy and Cosmology – Einstein’s
W. Kausch, M. Gitti, T. Erben and S. Schindler ESO Astrophysics Symposia, Relativistic Astrophysics Legacy and Cosmology – Einstein’s 326 (2008) https://doi.org/10.1007/978-3-540-74713-0_75