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:

Exploring the potential of Quantum Graph Neural Networks in analyzing Large-Scale Structure of Universe

Farida Farsian, Francesco Schilliró, Andrea Bulgarelli, Carlo Burigana, Vincenzo Cardone, Luca Cappelli, Irene Graziotti, Massimo Meneghetti, Giuseppe Murante, Nicoló Parmiggiani, Alessandro Rizzo, Giuseppe Sarracino, Roberto Scaramella, Vincenzo Testa and Tiziana Trombetti
Astronomy and Computing 56 101095 (2026)
https://doi.org/10.1016/j.ascom.2026.101095

A method for asteroid detection using convolutional neural networks on VST images

B. Y. Irureta-Goyena, E. Rachith, S. Hellmich, J.-P. Kneib, B. Altieri, C. Lemon, T. Saifollahi, O. Hainaut, W. Freudling, F. Dux, M. Micheli, F. Ocaña, P. Ramírez-Moreta, F. Courbin, L. Conversi, M. Millon, G. Verdoes Kleijn and M. Salzmann
Astronomy & Astrophysics 694 A49 (2025)
https://doi.org/10.1051/0004-6361/202452756

Gaia GraL: Gaia gravitational lens systems

Q. Petit, C. Ducourant, E. Slezak, A. Krone-Martins, C. Bœhm, T. Connor, L. Delchambre, S. G. Djorgovski, L. Galluccio, M. J. Graham, P. Jalan, S. A. Klioner, J. Klüter, F. Mignard, V. Negi, S. Scarano Jr, J. Sebastian den Brok, D. Sluse, D. Stern, J. Surdej, R. Teixeira, P. H. Vale-Cunha, D. J. Walton and J. Wambsganss
Astronomy & Astrophysics 696 A51 (2025)
https://doi.org/10.1051/0004-6361/202451690

Euclid: Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field

R. Pearce-Casey, B. C. Nagam, J. Wilde, V. Busillo, L. Ulivi, I. T. Andika, A. Manjón-García, L. Leuzzi, P. Matavulj, S. Serjeant, M. Walmsley, J. A. Acevedo Barroso, C. M. O’Riordan, B. Clément, C. Tortora, T. E. Collett, F. Courbin, R. Gavazzi, R. B. Metcalf, R. Cabanac, H. M. Courtois, J. Crook-Mansour, L. Delchambre, G. Despali, L. R. Ecker, et al.
Astronomy & Astrophysics 696 A214 (2025)
https://doi.org/10.1051/0004-6361/202453152

Euclid: Finding strong gravitational lenses in the early release observations using convolutional neural networks

B. C. Nagam, J. A. Acevedo Barroso, J. Wilde, I. T. Andika, A. Manjón-García, R. Pearce-Casey, D. Stern, J. W. Nightingale, L. A. Moustakas, K. McCarthy, E. Moravec, L. Leuzzi, K. Rojas, S. Serjeant, T. E. Collett, P. Matavulj, M. Walmsley, B. Clément, C. Tortora, R. Gavazzi, R. B. Metcalf, C. M. O’Riordan, G. Verdoes Kleijn, L. V. E. Koopmans, E. A. Valentijn, et al.
Astronomy & Astrophysics 702 A130 (2025)
https://doi.org/10.1051/0004-6361/202554132

Breaking the degeneracy in stellar spectral classification from single wide-band images

Ezequiel Centofanti, Samuel Farrens, Jean-Luc Starck, Tobías Liaudat, Alex Szapiro and Jennifer Pollack
Astronomy & Astrophysics 694 A228 (2025)
https://doi.org/10.1051/0004-6361/202452224

Euclid: Detecting Solar System objects in Euclid images and classifying them using Kohonen self-organising maps

A. A. Nucita, L. Conversi, A. Verdier, A. Franco, S. Sacquegna, M. Pöntinen, B. Altieri, B. Carry, F. De Paolis, F. Strafella, V. Orofino, M. Maiorano, V. Kansal, R. D. Vavrek, M. Miluzio, M. Granvik, V. Testa, N. Aghanim, S. Andreon, N. Auricchio, M. Baldi, S. Bardelli, E. Branchini, M. Brescia, J. Brinchmann, et al.
Astronomy & Astrophysics 694 A116 (2025)
https://doi.org/10.1051/0004-6361/202451767

ULISSE: Determination of the star formation rate and stellar mass based on the one-shot galaxy imaging technique

Olena Torbaniuk, Lars Doorenbos, Maurizio Paolillo, Stefano Cavuoti, Massimo Brescia and Giuseppe Longo
Astronomy & Astrophysics 701 A162 (2025)
https://doi.org/10.1051/0004-6361/202452704

Constraining cosmological parameters using bayesian MCMC method and artificial neural networks in modified theory of gravity

Lokesh Kumar Sharma, Suresh Parekh, Anil Kumar Yadav and Preeti Shrivastava
Indian Journal of Physics 99 (10) 3989 (2025)
https://doi.org/10.1007/s12648-025-03590-4

Additive Attention for Vetting Transiting Exoplanet Candidates

Àlvar Hernàndez-Carnerero, Miquel Sànchez-Marrè and Juan Carlos Morales
The Astronomical Journal 170 (1) 21 (2025)
https://doi.org/10.3847/1538-3881/add2f1

Classifying merger stages with adaptive deep learning and cosmological hydrodynamical simulations

Rosa de Graaff, Berta Margalef-Bentabol, Lingyu Wang, Antonio La Marca, William J. Pearson, Vicente Rodriguez-Gomez and Mike Walmsley
Astronomy & Astrophysics 697 A207 (2025)
https://doi.org/10.1051/0004-6361/202452659

Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning

Xingchen Zhou, Yan Gong, Xin Zhang, Nan Li, Xian-Min Meng, Xuelei Chen, Run Wen, Yunkun Han, Hu Zou, Xian Zhong Zheng, Xiaohu Yang, Hong Guo and Pengjie Zhang
The Astrophysical Journal 977 (1) 69 (2024)
https://doi.org/10.3847/1538-4357/ad8bbf

HOLISMOKES

R. Cañameras, S. Schuldt, Y. Shu, S. H. Suyu, S. Taubenberger, I. T. Andika, S. Bag, K. T. Inoue, A. T. Jaelani, L. Leal-Taixé, T. Meinhardt, A. Melo and A. More
Astronomy & Astrophysics 692 A72 (2024)
https://doi.org/10.1051/0004-6361/202347072

Systematic comparison of neural networks used in discovering strong gravitational lenses

Anupreeta More, Raoul Cañameras, Anton T Jaelani, Yiping Shu, Yuichiro Ishida, Kenneth C Wong, Kaiki Taro Inoue, Stefan Schuldt and Alessandro Sonnenfeld
Monthly Notices of the Royal Astronomical Society 533 (1) 525 (2024)
https://doi.org/10.1093/mnras/stae1597

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 McMahon
Space Science Reviews 220 (2) (2024)
https://doi.org/10.1007/s11214-024-01042-9

TEGLIE: Transformer encoders as strong gravitational lens finders in KiDS

M. Grespan, H. Thuruthipilly, A. Pollo, M. Lochner, M. Biesiada and V. Etsebeth
Astronomy & Astrophysics 688 A34 (2024)
https://doi.org/10.1051/0004-6361/202449929

On the detection and precise localization of merging black holes events through strong gravitational lensing

Ewoud Wempe, Léon V E Koopmans, A Renske A C Wierda, Otto A Hannuksela and Chris Van Den Broeck
Monthly Notices of the Royal Astronomical Society 530 (3) 3368 (2024)
https://doi.org/10.1093/mnras/stae1023

Measuring the substructure mass power spectrum of 23 SLACS strong galaxy–galaxy lenses with convolutional neural networks

Joshua Fagin, Georgios Vernardos, Grigorios Tsagkatakis, Yannis Pantazis, Anowar J Shajib and Matthew O’Dowd
Monthly Notices of the Royal Astronomical Society 532 (2) 2248 (2024)
https://doi.org/10.1093/mnras/stae1593

A Bayesian approach to strong lens finding in the era of wide-area surveys

Philip Holloway, Philip J Marshall, Aprajita Verma, Anupreeta More, Raoul Cañameras, Anton T Jaelani, Yuichiro Ishida and Kenneth C Wong
Monthly Notices of the Royal Astronomical Society 530 (2) 1297 (2024)
https://doi.org/10.1093/mnras/stae875

Automation of finding strong gravitational lenses in the Kilo Degree Survey with U – DenseLens (DenseLens  + Segmentation)

Bharath Chowdhary N, Léon V E Koopmans, Edwin A Valentijn, Gijs Verdoes Kleijn, Jelte T A de Jong, Nicola Napolitano, Rui Li, Crescenzo Tortora, Valerio Busillo and Yue Dong
Monthly Notices of the Royal Astronomical Society 533 (2) 1426 (2024)
https://doi.org/10.1093/mnras/stae1882

High-fidelity reconstruction of large-area damaged turbulent fields with a physically constrained generative adversarial network

Qinmin Zheng, Tianyi Li, Benteng Ma, Lin Fu and Xiaomeng Li
Physical Review Fluids 9 (2) (2024)
https://doi.org/10.1103/PhysRevFluids.9.024608

Shedding light on low-surface-brightness galaxies in dark energy surveys with transformer models

H. Thuruthipilly, Junais, A. Pollo, U. Sureshkumar, M. Grespan, P. Sawant, K. Małek and A. Zadrozny
Astronomy & Astrophysics 682 A4 (2024)
https://doi.org/10.1051/0004-6361/202347649

A thorough investigation of the prospects of eLISA in addressing the Hubble tension: Fisher forecast, MCMC and Machine Learning

Rahul Shah, Arko Bhaumik, Purba Mukherjee and Supratik Pal
Journal of Cosmology and Astroparticle Physics 2023 (06) 038 (2023)
https://doi.org/10.1088/1475-7516/2023/06/038

Testing machine learning algorithms for the prediction of depositional fluxes of the radionuclides 7Be, 210Pb and 40K

P. De La Torre Luque, C. Dueñas, E. Gordo and S. Cañete
Journal of Environmental Radioactivity 265 107213 (2023)
https://doi.org/10.1016/j.jenvrad.2023.107213

The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

M. Huertas-Company and F. Lanusse
Publications of the Astronomical Society of Australia 40 (2023)
https://doi.org/10.1017/pasa.2022.55

Anisotropic strong lensing as a probe of dark matter self-interactions

Birendra Dhanasingham, Francis-Yan Cyr-Racine, Charlie Mace, Annika H G Peter and Andrew Benson
Monthly Notices of the Royal Astronomical Society 526 (4) 5455 (2023)
https://doi.org/10.1093/mnras/stad3099

Processing of electrical resistivity tomography data using convolutional neural network in ERT-NET architectures

Puguh Hiskiawan, Chien-Chih Chen and Zheng-Kai Ye
Arabian Journal of Geosciences 16 (10) (2023)
https://doi.org/10.1007/s12517-023-11690-w

DeepGraviLens: a multi-modal architecture for classifying gravitational lensing data

Nicolò Oreste Pinciroli Vago and Piero Fraternali
Neural Computing and Applications 35 (26) 19253 (2023)
https://doi.org/10.1007/s00521-023-08766-9

On the application of machine learning in astronomy and astrophysics: A text‐mining‐based scientometric analysis

José‐Víctor Rodríguez, Ignacio Rodríguez‐Rodríguez and Wai Lok Woo
WIREs Data Mining and Knowledge Discovery 12 (5) (2022)
https://doi.org/10.1002/widm.1476

Likelihood-free Inference with the Mixture Density Network

Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma and Jun-Qing Xia
The Astrophysical Journal Supplement Series 262 (1) 24 (2022)
https://doi.org/10.3847/1538-4365/ac7da1

Recovering the CMB Signal with Machine Learning

Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li and Jun-Feng Li
The Astrophysical Journal Supplement Series 260 (1) 13 (2022)
https://doi.org/10.3847/1538-4365/ac5f4a

Astronomical big data processing using machine learning: A comprehensive review

Snigdha Sen, Sonali Agarwal, Pavan Chakraborty and Krishna Pratap Singh
Experimental Astronomy 53 (1) 1 (2022)
https://doi.org/10.1007/s10686-021-09827-4

Identification of Grand-design and Flocculent spirals from SDSS using deep convolutional neural network

Suman Sarkar, Ganesh Narayanan, Arunima Banerjee and Prem Prakash
Monthly Notices of the Royal Astronomical Society 518 (1) 1022 (2022)
https://doi.org/10.1093/mnras/stac3096

The evolution of barred galaxies in the EAGLE simulations

Mitchell K Cavanagh, Kenji Bekki, Brent A Groves and Joel Pfeffer
Monthly Notices of the Royal Astronomical Society 510 (4) 5164 (2022)
https://doi.org/10.1093/mnras/stab3786

AI-driven spatio-temporal engine for finding gravitationally lensed type Ia supernovae

Doogesh Kodi Ramanah, Nikki Arendse and Radosław Wojtak
Monthly Notices of the Royal Astronomical Society 512 (4) 5404 (2022)
https://doi.org/10.1093/mnras/stac838

Finding quadruply imaged quasars with machine learning – I. Methods

A Akhazhanov, A More, A Amini, C Hazlett, T Treu, S Birrer, A Shajib, K Liao, C Lemon, A Agnello, B Nord, M Aguena, S Allam, F Andrade-Oliveira, J Annis, D Brooks, E Buckley-Geer, D L Burke, A Carnero Rosell, M Carrasco Kind, J Carretero, A Choi, C Conselice, M Costanzi, L N da Costa, et al.
Monthly Notices of the Royal Astronomical Society 513 (2) 2407 (2022)
https://doi.org/10.1093/mnras/stac925

A machine learning based approach to gravitational lens identification with the International LOFAR Telescope

S Rezaei, J P McKean, M Biehl, W de Roo and A Lafontaine
Monthly Notices of the Royal Astronomical Society 517 (1) 1156 (2022)
https://doi.org/10.1093/mnras/stac2078

ulisse: A tool for one-shot sky exploration and its application for detection of active galactic nuclei

Lars Doorenbos, Olena Torbaniuk, Stefano Cavuoti, et al.
Astronomy & Astrophysics 666 A171 (2022)
https://doi.org/10.1051/0004-6361/202243900

Finding strong gravitational lenses through self-attention

Hareesh Thuruthipilly, Adam Zadrozny, Agnieszka Pollo and Marek Biesiada
Astronomy & Astrophysics 664 A4 (2022)
https://doi.org/10.1051/0004-6361/202142463

The galaxy morphology–density relation in the EAGLE simulation

Joel Pfeffer, Mitchell K Cavanagh, Kenji Bekki, et al.
Monthly Notices of the Royal Astronomical Society 518 (4) 5260 (2022)
https://doi.org/10.1093/mnras/stac3466

A comparative study of convolutional neural networks for the detection of strong gravitational lensing

Daniel Magro, Kristian Zarb Adami, Andrea DeMarco, Simone Riggi and Eva Sciacca
Monthly Notices of the Royal Astronomical Society 505 (4) 6155 (2021)
https://doi.org/10.1093/mnras/stab1635

Construction of a far-ultraviolet all-sky map from an incomplete survey: application of a deep learning algorithm

Young-Soo Jo, Yeon-Ju Choi, Min-Gi Kim, et al.
Monthly Notices of the Royal Astronomical Society 502 (3) 3200 (2021)
https://doi.org/10.1093/mnras/stab066

Morphological classification of galaxies with deep learning: comparing 3-way and 4-way CNNs

Mitchell K Cavanagh, Kenji Bekki and Brent A Groves
Monthly Notices of the Royal Astronomical Society 506 (1) 659 (2021)
https://doi.org/10.1093/mnras/stab1552

SILVERRUSH X: Machine Learning-aided Selection of 9318 LAEs at z = 2.2, 3.3, 4.9, 5.7, 6.6, and 7.0 from the HSC SSP and CHORUS Survey Data

Yoshiaki Ono, Ryohei Itoh, Takatoshi Shibuya, Masami Ouchi, Yuichi Harikane, Satoshi Yamanaka, Akio K. Inoue, Toshiyuki Amagasa, Daichi Miura, Maiki Okura, Kazuhiro Shimasaku, Ikuru Iwata, Yoshiaki Taniguchi, Seiji Fujimoto, Masanori Iye, Anton T. Jaelani, Nobunari Kashikawa, Shotaro Kikuchihara, Satoshi Kikuta, Masakazu A. R. Kobayashi, Haruka Kusakabe, Chien-Hsiu Lee, Yongming Liang, Yoshiki Matsuoka, Rieko Momose, et al.
The Astrophysical Journal 911 (2) 78 (2021)
https://doi.org/10.3847/1538-4357/abea15

Weak-lensing Mass Reconstruction of Galaxy Clusters with a Convolutional Neural Network

Sungwook E. 성욱 Hong 홍, Sangnam Park, M. James Jee, Dongsu Bak and Sangjun Cha
The Astrophysical Journal 923 (2) 266 (2021)
https://doi.org/10.3847/1538-4357/ac3090

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

A proof-of-concept neural network for inferring parameters of a black hole from partial interferometric images of its shadow

A.A. Popov, V.N. Strokov and A.A. Surdyaev
Astronomy and Computing 36 100467 (2021)
https://doi.org/10.1016/j.ascom.2021.100467

Deep learning for strong lensing search: tests of the convolutional neural networks and new candidates from KiDS DR3

Zizhao He, Xinzhong Er, Qian Long, et al.
Monthly Notices of the Royal Astronomical Society 497 (1) 556 (2020)
https://doi.org/10.1093/mnras/staa1917

Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with a Hubble Parameter and SNe Ia

Guo-Jian Wang, Xiao-Jiao Ma, Si-Yao Li and Jun-Qing Xia
The Astrophysical Journal Supplement Series 246 (1) 13 (2020)
https://doi.org/10.3847/1538-4365/ab620b

ECoPANN: A Framework for Estimating Cosmological Parameters Using Artificial Neural Networks

Guo-Jian Wang, Si-Yao Li and Jun-Qing Xia
The Astrophysical Journal Supplement Series 249 (2) 25 (2020)
https://doi.org/10.3847/1538-4365/aba190

Probing Neural Networks for the Gamma/Hadron Separation of the Cherenkov Telescope Array

E Lyard, R Walter, V Sliusar and N Produit
Journal of Physics: Conference Series 1525 (1) 012084 (2020)
https://doi.org/10.1088/1742-6596/1525/1/012084

Deep-Learning Study of the 21-cm Differential Brightness Temperature During the Epoch of Reionization

Yungi Kwon, Sungwook E. Hong and Inkyu Park
Journal of the Korean Physical Society 77 (1) 49 (2020)
https://doi.org/10.3938/jkps.77.49

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

Hybridizing Evolutionary Computation and Deep Neural Networks: An Approach to Handwriting Recognition Using Committees and Transfer Learning

Alejandro Baldominos, Yago Saez, Pedro Isasi and Michele Scarpiniti
Complexity 2019 (1) (2019)
https://doi.org/10.1155/2019/2952304

Deep-CEE I: fishing for galaxy clusters with deep neural nets

Matthew C Chan and John P Stott
Monthly Notices of the Royal Astronomical Society 490 (4) 5770 (2019)
https://doi.org/10.1093/mnras/stz2936

A deep learning model to emulate simulations of cosmic reionization

Jonathan Chardin, Grégoire Uhlrich, Dominique Aubert, et al.
Monthly Notices of the Royal Astronomical Society 490 (1) 1055 (2019)
https://doi.org/10.1093/mnras/stz2605

Data-driven Reconstruction of Gravitationally Lensed Galaxies Using Recurrent Inference Machines

Warren R. Morningstar, Laurence Perreault Levasseur, Yashar D. Hezaveh, Roger Blandford, Phil Marshall, Patrick Putzky, Thomas D. Rueter, Risa Wechsler and Max Welling
The Astrophysical Journal 883 (1) 14 (2019)
https://doi.org/10.3847/1538-4357/ab35d7

Radio Galaxy Zoo:Claran– a deep learning classifier for radio morphologies

Chen Wu, Oiwei Ivy Wong, Lawrence Rudnick, et al.
Monthly Notices of the Royal Astronomical Society 482 (1) 1211 (2019)
https://doi.org/10.1093/mnras/sty2646

Detecting Solar system objects with convolutional neural networks

Maggie Lieu, Luca Conversi, Bruno Altieri and Benoît Carry
Monthly Notices of the Royal Astronomical Society 485 (4) 5831 (2019)
https://doi.org/10.1093/mnras/stz761

Neural network-based anomaly detection for high-resolution X-ray spectroscopy

Y Ichinohe and S Yamada
Monthly Notices of the Royal Astronomical Society 487 (2) 2874 (2019)
https://doi.org/10.1093/mnras/stz1528