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):
6067 (2024)
https://doi.org/10.1109/CVPR52733.2024.00580
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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