Convolutional neural networks on the HEALPix sphere: a pixel-based algorithm and its application to CMB data analysis
A&A, 628 (2019) A129
Published online: 2019-08-20
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):
CMB delensing with deep learning
Shulei Ni, Yichao Li and Xin ZhangCommunications in Theoretical Physics 78 (3) 035405 (2026)
https://doi.org/10.1088/1572-9494/ae1940
Bayesian Inference of Primordial Magnetic Field Parameters from CMB with Spherical Graph Neural Networks
Juan Alejandro Pinto Castro, Héctor J. Hortúa, Jorge Enrique García-Farieta and Roger Anderson HurtadoUniverse 12 (2) 34 (2026)
https://doi.org/10.3390/universe12020034
s-r2d2: a spherical extension of the r2d2 deep neural network series paradigm for wide-field radio-interferometric imaging
A Tajja, A Aghabiglou, E Tolley, J -P Kneib, J -P Thiran and Y WiauxMonthly Notices of the Royal Astronomical Society 542 (1) 426 (2025)
https://doi.org/10.1093/mnras/staf1082
Reducing false positives in strong lens detection through effective augmentation and ensemble learning
Samira Rezaei, Amirmohammad Chegeni, Bharath Chowdhary Nagam, J P McKean, Mitra Baratchi, Koen Kuijken and Léon V E KoopmansMonthly Notices of the Royal Astronomical Society 538 (2) 1081 (2025)
https://doi.org/10.1093/mnras/staf327
Recovering CMB polarization maps with neural networks: performance in realistic simulations
J.M. Casas, L. Bonavera, J. González-Nuevo, G. Puglisi, C. Baccigalupi, S.R. Cabo, M.M. Cueli, D. Crespo, C. González-Gutiérrez and F.J. de CosJournal of Cosmology and Astroparticle Physics 2025 (10) 063 (2025)
https://doi.org/10.1088/1475-7516/2025/10/063
Deep Needlet: a CNN based full sky component separation method in Needlet space
Debabrata AdakJournal of Cosmology and Astroparticle Physics 2025 (08) 058 (2025)
https://doi.org/10.1088/1475-7516/2025/08/058
Towards detecting primordial non-Gaussianity in the CMB using spherical convolutional neural networks
Jorik Melsen, Thomas Flöss and P Daniel MeerburgMonthly Notices of the Royal Astronomical Society 541 (4) 3269 (2025)
https://doi.org/10.1093/mnras/staf1097
Foreground Removal in Ground-based CMB Observations Using a Transformer Model
Ye-Peng Yan, Si-Yu Li, Yang Liu, Jun-Qing Xia and Hong LiThe Astrophysical Journal Supplement Series 281 (2) 47 (2025)
https://doi.org/10.3847/1538-4365/ae12f1
9418 (2025)
https://doi.org/10.1109/ICCV51701.2025.00879
Self-supervised component separation for the extragalactic submillimetre sky
V. Bonjean, H. Tanimura, N. Aghanim, T. Bonnaire and M. DouspisAstronomy & Astrophysics 686 A91 (2024)
https://doi.org/10.1051/0004-6361/202245624
FORSE+: Simulating non-Gaussian CMB foregrounds at 3 arcmin in a stochastic way based on a generative adversarial network
Jian Yao, Nicoletta Krachmalnicoff, Marianna Foschi, Giuseppe Puglisi and Carlo BaccigalupiAstronomy & Astrophysics 686 A290 (2024)
https://doi.org/10.1051/0004-6361/202449827
6067 (2024)
https://doi.org/10.1109/CVPR52733.2024.00580
Inference of the optical depth to reionization τ from Planck CMB maps with convolutional neural networks
Kevin Wolz, Nicoletta Krachmalnicoff and Luca PaganoAstronomy & Astrophysics 676 A30 (2023)
https://doi.org/10.1051/0004-6361/202345982
6134 (2023)
https://doi.org/10.1109/CVPRW59228.2023.00653
Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning
A. Amerio, A. Cuoco and N. FornengoJournal of Cosmology and Astroparticle Physics 2023 (09) 029 (2023)
https://doi.org/10.1088/1475-7516/2023/09/029
A seamless approach for evaluating climate models across spatial scales
Alex Chang, Hugo Lee, Rong Fu and Qi TangFrontiers in Earth Science 11 (2023)
https://doi.org/10.3389/feart.2023.1245815
Constraining the polarisation flux density and angle of point sources by training a convolutional neural network
J. M. Casas, L. Bonavera, J. González-Nuevo, et al.Astronomy & Astrophysics 670 A76 (2023)
https://doi.org/10.1051/0004-6361/202244424
Analysing arrival directions of ultra-high-energy cosmic rays with convolutional neural networks
Oleg Kalashev, Maxim Pshirkov and Mikhail ZotovJournal of Physics: Conference Series 2438 (1) 012067 (2023)
https://doi.org/10.1088/1742-6596/2438/1/012067
OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression
Navid Mahmoudian Bidgoli, Roberto G. de A. Azevedo, Thomas Maugey, Aline Roumy and Pascal FrossardIEEE Transactions on Image Processing 31 5813 (2022)
https://doi.org/10.1109/TIP.2022.3202357
Exoplanet cartography using convolutional neural networks
K. Meinke, D. M. Stam and P. M. VisserAstronomy & Astrophysics 664 A59 (2022)
https://doi.org/10.1051/0004-6361/202142932
CENN: A fully convolutional neural network for CMB recovery in realistic microwave sky simulations
J. M. Casas, L. Bonavera, J. González-Nuevo, et al.Astronomy & Astrophysics 666 A89 (2022)
https://doi.org/10.1051/0004-6361/202243450
A novel cosmic filament catalogue from SDSS data
Javier Carrón Duque, Marina Migliaccio, Domenico Marinucci and Nicola VittorioAstronomy & Astrophysics 659 A166 (2022)
https://doi.org/10.1051/0004-6361/202141538
Approximate Bayesian neural Doppler imaging
A. Asensio Ramos, C. J. Díaz Baso and O. KochukhovAstronomy & Astrophysics 658 A162 (2022)
https://doi.org/10.1051/0004-6361/202142027
DeepSZ: identification of Sunyaev–Zel’dovich galaxy clusters using deep learning
Z Lin, N Huang, C Avestruz, et al.Monthly Notices of the Royal Astronomical Society 507 (3) 4149 (2021)
https://doi.org/10.1093/mnras/stab2229
A generative model of galactic dust emission using variational autoencoders
Ben Thorne, Lloyd Knox and Karthik PrabhuMonthly Notices of the Royal Astronomical Society 504 (2) 2603 (2021)
https://doi.org/10.1093/mnras/stab1011
A convolutional-neural-network estimator of CMB constraints on dark matter energy injection
Wei-Chih Huang, Jui-Lin Kuo and Yue-Lin Sming TsaiJournal of Cosmology and Astroparticle Physics 2021 (06) 025 (2021)
https://doi.org/10.1088/1475-7516/2021/06/025
Emulation of Cosmological Mass Maps with Conditional Generative Adversarial Networks
Nathanaël Perraudin, Sandro Marcon, Aurelien Lucchi and Tomasz KacprzakFrontiers in Artificial Intelligence 4 (2021)
https://doi.org/10.3389/frai.2021.673062
Planet cartography with neural learned regularization
A. Asensio Ramos and E. PalléAstronomy & Astrophysics 646 A4 (2021)
https://doi.org/10.1051/0004-6361/202040066
ForSE: A GAN-based Algorithm for Extending CMB Foreground Models to Subdegree Angular Scales
Nicoletta Krachmalnicoff and Giuseppe PuglisiThe Astrophysical Journal 911 (1) 42 (2021)
https://doi.org/10.3847/1538-4357/abe71c
Computer Vision – ACCV 2020
Chao Zhang, Ignas Budvytis, Stephan Liwicki and Roberto CipollaLecture Notes in Computer Science, Computer Vision – ACCV 2020 12625 334 (2021)
https://doi.org/10.1007/978-3-030-69538-5_21
Foreground model recognition through Neural Networks for CMB B-mode observations
F. Farsian, N. Krachmalnicoff and C. BaccigalupiJournal of Cosmology and Astroparticle Physics 2020 (07) 017 (2020)
https://doi.org/10.1088/1475-7516/2020/07/017
Identifying nearby sources of ultra-high-energy cosmic rays with deep learning
Oleg Kalashev, Maxim Pshirkov and Mikhail ZotovJournal of Cosmology and Astroparticle Physics 2020 (11) 005 (2020)
https://doi.org/10.1088/1475-7516/2020/11/005
Parameter estimation for the cosmic microwave background with Bayesian neural networks
Héctor J. Hortúa, Riccardo Volpi, Dimitri Marinelli and Luigi MalagòPhysical Review D 102 (10) (2020)
https://doi.org/10.1103/PhysRevD.102.103509
Classifying CMB time-ordered data through deep neural networks
Karim Pichara, Rolando Dünner, Loïc Maurin and Felipe RojasMonthly Notices of the Royal Astronomical Society 494 (3) 3741 (2020)
https://doi.org/10.1093/mnras/staa1009
The First Application of Neural Networks to the Analysis of the TUS Orbital Detector Data
M. Yu. Zotov and D. B. SokolinskiyMoscow University Physics Bulletin 75 (6) 657 (2020)
https://doi.org/10.3103/S0027134920060235
Power of halometry
Siddharth Mishra-Sharma, Ken Van Tilburg and Neal WeinerPhysical Review D 102 (2) (2020)
https://doi.org/10.1103/PhysRevD.102.023026
Inpainting Galactic Foreground Intensity and Polarization Maps Using Convolutional Neural Networks
Giuseppe Puglisi and Xiran BaiThe Astrophysical Journal 905 (2) 143 (2020)
https://doi.org/10.3847/1538-4357/abc47c
Full-sky Cosmic Microwave Background Foreground Cleaning Using Machine Learning
Matthew A. Petroff, Graeme E. Addison, Charles L. Bennett and Janet L. WeilandThe Astrophysical Journal 903 (2) 104 (2020)
https://doi.org/10.3847/1538-4357/abb9a7
DeepCMB: Lensing reconstruction of the cosmic microwave background with deep neural networks
J. Caldeira, W.L.K. Wu, B. Nord, C. Avestruz, S. Trivedi and K.T. StoryAstronomy and Computing 28 100307 (2019)
https://doi.org/10.1016/j.ascom.2019.100307
DeepSphere: Efficient spherical convolutional neural network with HEALPix sampling for cosmological applications
N. Perraudin, M. Defferrard, T. Kacprzak and R. SgierAstronomy and Computing 27 130 (2019)
https://doi.org/10.1016/j.ascom.2019.03.004