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).
A gradient boosting and broadband approach to finding Lyman-α emitting galaxies beyond narrowband surveys
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Ensemble-learning for pressure prediction in vacuum circuit breaker using feature fusion of laser-induced plasma spectra and images
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Semi-supervised classification of stars, galaxies and quasars using K-means and random-forest approaches
Hybrid-z: Enhancing the Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning
Anjitha John William, Priyanka Jalan, Maciej Bilicki, Wojciech A. Hellwing, Hareesh Thuruthipilly and Szymon J. Nakoneczny Astronomy & Astrophysics 698 A276 (2025) https://doi.org/10.1051/0004-6361/202453576
Searching for high-redshift quasars with the Photometric Vision Quasar Network (PVQNet)
Chen Zhang, Wenyu Wang, Meixia Qu, Bin Jiang and YanXia Zhang Publications of the Astronomical Society of Japan (2025) https://doi.org/10.1093/pasj/psaf061
Machine Learning Classification of COSMOS2020 Galaxies: Quiescent versus Star-forming
Vahid Asadi, Nima Chartab, Akram Hasani Zonoozi, Hosein Haghi, Ghassem Gozaliasl, Aryana Haghjoo and Bahram Mobasher The Astrophysical Journal 993(1) 123 (2025) https://doi.org/10.3847/1538-4357/ae0a2c
Euclid preparation
A. Humphrey, P. A. C. Cunha, L. Bisigello, C. Tortora, M. Bolzonella, L. Pozzetti, M. Baes, B. R. Granett, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, S. Bardelli, C. Bodendorf, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, et al. Astronomy & Astrophysics 702 A74 (2025) https://doi.org/10.1051/0004-6361/202452468
Fine-grained photometric classification using multi-model fusion method with redshift estimation
Identifying type II quasars at intermediate redshift with few-shot learning photometric classification
P. A. C. Cunha, A. Humphrey, J. Brinchmann, S. G. Morais, R. Carvajal, J. M. Gomes, I. Matute and A. Paulino-Afonso Astronomy & Astrophysics 687 A269 (2024) https://doi.org/10.1051/0004-6361/202346426
Euclid preparation
L. Bisigello, M. Massimo, C. Tortora, S. Fotopoulou, V. Allevato, M. Bolzonella, C. Gruppioni, L. Pozzetti, G. Rodighiero, S. Serjeant, P. A. C. Cunha, L. Gabarra, A. Feltre, A. Humphrey, F. La Franca, H. Landt, F. Mannucci, I. Prandoni, M. Radovich, F. Ricci, M. Salvato, F. Shankar, D. Stern, L. Spinoglio, D. Vergani, et al. Astronomy & Astrophysics 691 A1 (2024) https://doi.org/10.1051/0004-6361/202450446
Machine learning based stellar classification with highly sparse photometry data
Transferring spectroscopic stellar labels to 217 million Gaia DR3 XP stars with SHBoost
A. Khalatyan, F. Anders, C. Chiappini, A. B. A. Queiroz, S. Nepal, M. dal Ponte, C. Jordi, G. Guiglion, M. Valentini, G. Torralba Elipe, M. Steinmetz, M. Pantaleoni-González, S. Malhotra, Ó. Jiménez-Arranz, H. Enke, L. Casamiquela and J. Ardèvol Astronomy & Astrophysics 691 A98 (2024) https://doi.org/10.1051/0004-6361/202451427
Photometric Redshift Estimation of Quasars by a Cross-modal Contrast Learning Method
A. Enia, M. Bolzonella, L. Pozzetti, A. Humphrey, P. A. C. Cunha, W. G. Hartley, F. Dubath, S. Paltani, X. Lopez Lopez, S. Quai, S. Bardelli, L. Bisigello, S. Cavuoti, G. De Lucia, M. Ginolfi, A. Grazian, M. Siudek, C. Tortora, G. Zamorani, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, et al. Astronomy & Astrophysics 691 A175 (2024) https://doi.org/10.1051/0004-6361/202451425
The regression for the redshifts of galaxies in SDSS DR18
Wen Xiao-Qing, Yin Hong-Wei, Liu Feng-Hua, Yang Shang-Tao, Zhu Yi-Rong, Yang Jin-Meng, Su Zi-Jie and Guan Bing Chinese Journal of Physics 90 542 (2024) https://doi.org/10.1016/j.cjph.2024.05.045
Dynamic bond stress-slip relationship of steel reinforcing bars in concrete based on XGBoost algorithm
Xinxin Li, Zhaolun Ran, Dan Zheng, Chenghe Hu, Zhangchen Qin, Haicui Wang, Zhao Wang and Pengfei Li Journal of Building Engineering 84 108368 (2024) https://doi.org/10.1016/j.jobe.2023.108368
Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles
Juan C. Vizcarra, Thomas M. Pearce, Brittany N. Dugger, Michael J. Keiser, Marla Gearing, John F. Crary, Evan J. Kiely, Meaghan Morris, Bartholomew White, Jonathan D. Glass, Kurt Farrell and David A. Gutman Acta Neuropathologica Communications 11(1) (2023) https://doi.org/10.1186/s40478-023-01691-x
Measurement methods for gamma-ray bursts redshifts
Mengci Li, Zhe Kang, Chao Wu, Chengzhi Liu, Jirong Mao, Zhenwei Li, Shiyu Deng, Bingli Niu and Ping Jiang Frontiers in Astronomy and Space Sciences 10 (2023) https://doi.org/10.3389/fspas.2023.1124317
Selection of powerful radio galaxies with machine learning
R. Carvajal, I. Matute, J. Afonso, R. P. Norris, K. J. Luken, P. Sánchez-Sáez, P. A. C. Cunha, A. Humphrey, H. Messias, S. Amarantidis, D. Barbosa, H. A. Cruz, H. Miranda, A. Paulino-Afonso and C. Pappalardo Astronomy & Astrophysics 679 A101 (2023) https://doi.org/10.1051/0004-6361/202245770
Safely advancing a spacefaring humanity with artificial intelligence
Catherine E. Richards, Tom Cernev, Asaf Tzachor, Gustavs Zilgalvis and Bartu Kaleagasi Frontiers in Space Technologies 4 (2023) https://doi.org/10.3389/frspt.2023.1199547
PhotoRedshift-MML: A multimodal machine learning method for estimating photometric redshifts of quasars
Machine-learning classification of astronomical sources: estimating F1-score in the absence of ground truth
A Humphrey, W Kuberski, J Bialek, N Perrakis, W Cools, N Nuyttens, H Elakhrass and P A C Cunha Monthly Notices of the Royal Astronomical Society: Letters 517(1) L116 (2022) https://doi.org/10.1093/mnrasl/slac120