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:

An extended catalogue of galaxy morphology using deep learning in southern photometric local universe survey data release 3

C R Bom, A Cortesi, U Ribeiro, L O Dias, K Kelkar, A V Smith Castelli, L Santana-Silva, V Lopes-Silva, T S Gonçalves, L R Abramo, E V R Lima, F Almeida-Fernandes, L Espinosa, L Li, M L Buzzo, C Mendes de Oliveira, L Sodré, F Ferrari, A Alvarez-Candal, M Grossi, E Telles, S Torres-Flores, S V Werner, A Kanaan, T Ribeiro and W Schoenell
Monthly Notices of the Royal Astronomical Society 528 (3) 4188 (2024)
https://doi.org/10.1093/mnras/stad3956

Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers

Peng Jia, Ruiqi Sun, Nan Li, Yu Song, Runyu Ning, Hongyan Wei and Rui Luo
The Astronomical Journal 165 (1) 26 (2023)
https://doi.org/10.3847/1538-3881/aca1c2

Identification of Galaxy–Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning

E. A. Zaborowski, A. Drlica-Wagner, F. Ashmead, J. F. Wu, R. Morgan, C. R. Bom, A. J. Shajib, S. Birrer, W. Cerny, E. J. Buckley-Geer, B. Mutlu-Pakdil, P. S. Ferguson, K. Glazebrook, S. J. Gonzalez Lozano, Y. Gordon, M. Martinez, V. Manwadkar, J. O’Donnell, J. Poh, A. Riley, J. D. Sakowska, L. Santana-Silva, B. X. Santiago, D. Sluse, C. Y. Tan, et al.
The Astrophysical Journal 954 (1) 68 (2023)
https://doi.org/10.3847/1538-4357/ace4ba

A deep learning based astronomical target detection framework for multi-colour photometry sky survey projects

P. Jia, Y. Zheng, M. Wang and Z. Yang
Astronomy and Computing 42 100687 (2023)
https://doi.org/10.1016/j.ascom.2023.100687

The Dark Energy Survey Bright Arcs Survey: Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey 5000 Square Degree Footprint

J. H. O’Donnell, R. D. Wilkinson, H. T. Diehl, C. Aros-Bunster, K. Bechtol, S. Birrer, E. J. Buckley-Geer, A. Carnero Rosell, M. Carrasco Kind, L. N. da Costa, S. J. Gonzalez Lozano, R. A. Gruendl, M. Hilton, H. Lin, K. A. Lindgren, J. Martin, A. Pieres, E. S. Rykoff, I. Sevilla-Noarbe, E. Sheldon, C. Sifón, D. L. Tucker, B. Yanny, T. M. C. Abbott, M. Aguena, et al.
The Astrophysical Journal Supplement Series 259 (1) 27 (2022)
https://doi.org/10.3847/1538-4365/ac470b

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

Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting

James Pearson, Jacob Maresca, Nan Li and Simon Dye
Monthly Notices of the Royal Astronomical Society 505 (3) 4362 (2021)
https://doi.org/10.1093/mnras/stab1547

Nonsequential neural network for simultaneous, consistent classification, and photometric redshifts of OTELO galaxies

J. A. de Diego, J. Nadolny, Á. Bongiovanni, et al.
Astronomy & Astrophysics 655 A56 (2021)
https://doi.org/10.1051/0004-6361/202141360

Deep learning Blazar classification based on multifrequency spectral energy distribution data

Bernardo M O Fraga, Ulisses Barres de Almeida, Clécio R Bom, et al.
Monthly Notices of the Royal Astronomical Society 505 (1) 1268 (2021)
https://doi.org/10.1093/mnras/stab1349

Deep Learning assessment of galaxy morphology in S-PLUS Data Release 1

C R Bom, A Cortesi, G Lucatelli, et al.
Monthly Notices of the Royal Astronomical Society 507 (2) 1937 (2021)
https://doi.org/10.1093/mnras/stab1981

Auto-identification of unphysical source reconstructions in strong gravitational lens modelling

Jacob Maresca, Simon Dye and Nan Li
Monthly Notices of the Royal Astronomical Society 503 (2) 2229 (2021)
https://doi.org/10.1093/mnras/stab387

Identifying strong lenses with unsupervised machine learning using convolutional autoencoder

Robert B Metcalf, Simon Dye, Alfonso Aragón-Salamanca, et al.
Monthly Notices of the Royal Astronomical Society 494 (3) 3750 (2020)
https://doi.org/10.1093/mnras/staa1015

ROGER: Reconstructing orbits of galaxies in extreme regions using machine learning techniques

Martín de los Rios, Héctor J Martínez, Valeria Coenda, et al.
Monthly Notices of the Royal Astronomical Society 500 (2) 1784 (2020)
https://doi.org/10.1093/mnras/staa3339

The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples

James Pearson, Nan Li and Simon Dye
Monthly 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 Luo
The 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 Maturi
Monthly Notices of the Royal Astronomical Society 482 (2) 1824 (2019)
https://doi.org/10.1093/mnras/sty2784

Derivation of NARX models by expanding activation functions in neural networks

Hidenori Inaoka, Kozue Kobayashi, Satoru Nebuya, Hiroshi Kumagai, Harukazu Tsuruta and Yutaka Fukuoka
IEEJ Transactions on Electrical and Electronic Engineering 14 (8) 1209 (2019)
https://doi.org/10.1002/tee.22920

Auto-detection of strong gravitational lenses using convolutional neural networks

James Pearson, Clara Pennock and Tom Robinson
Emergent Scientist 2 1 (2018)
https://doi.org/10.1051/emsci/2017010

CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding

François Lanusse, Quanbin Ma, Nan Li, et al.
Monthly Notices of the Royal Astronomical Society 473 (3) 3895 (2018)
https://doi.org/10.1093/mnras/stx1665

Strong lensing cross-sections for isothermal models. I. Finite source effects in the circular case

Vanessa P de Freitas, Martin Makler and Habib S Dúmet-Montoya
Monthly Notices of the Royal Astronomical Society 481 (2) 2189 (2018)
https://doi.org/10.1093/mnras/sty2412

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 DES Bright Arcs Survey: Hundreds of Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey Science Verification and Year 1 Observations

H. T. Diehl, E. J. Buckley-Geer, K. A. Lindgren, B. Nord, H. Gaitsch, S. Gaitsch, H. Lin, S. Allam, T. E. Collett, C. Furlanetto, M. S. S. Gill, A. More, J. Nightingale, C. Odden, A. Pellico, D. L. Tucker, L. N. da Costa, A. Fausti Neto, N. Kuropatkin, M. Soares-Santos, B. Welch, Y. Zhang, J. A. Frieman, F. B. Abdalla, J. Annis, et al.
The Astrophysical Journal Supplement Series 232 (1) 15 (2017)
https://doi.org/10.3847/1538-4365/aa8667

VICS82: The VISTA–CFHT Stripe 82 Near-infrared Survey

J. E. Geach, Y.-T. Lin, M. Makler, J.-P. Kneib, N. P. Ross, W.-H. Wang, B.-C. Hsieh, A. Leauthaud, K. Bundy, H. J. McCracken, J. Comparat, G. B. Caminha, P. Hudelot, L. Lin, L. Van Waerbeke, M. E. S. Pereira and D. Mast
The Astrophysical Journal Supplement Series 231 (1) 7 (2017)
https://doi.org/10.3847/1538-4365/aa74b6

Finding strong lenses in CFHTLS using convolutional neural networks

C. Jacobs, K. Glazebrook, T. Collett, A. More and C. McCarthy
Monthly Notices of the Royal Astronomical Society 471 (1) 167 (2017)
https://doi.org/10.1093/mnras/stx1492