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).
Prediction of Stellar Mass and Star Formation Rate for Low-redshift Galaxies in the DESI Legacy Imaging Surveys Using Deep Learning
Guang-Jun Yang, Li-Li Wang, Ning Gai, Yan-Ke Tang and Jia-Bao Feng Research in Astronomy and Astrophysics 26(1) 017002 (2026) https://doi.org/10.1088/1674-4527/ae15e9
A Value-added Physical Properties Catalog for Low-redshift Galaxies from DESI Legacy Imaging Surveys DR10
Shirui Wei, Changhua Li, Yanxia Zhang, Chenzhou Cui, Jinghang Shi, Wujun Shao, Zihan Kang, Yongheng Zhao and Maoyuan Huang The Astrophysical Journal Supplement Series 284(2) 45 (2026) https://doi.org/10.3847/1538-4365/ae6242
Machine learning to predict star formation rates and stellar masses from photometric data of galaxies
Estimations of dark matter fractions for ETGs using the broken-power-law model and machine learning techniques
Rohan Shankar, Adithya Prakash and Aarya Mehta Monthly Notices of the Royal Astronomical Society 540(3) 2269 (2025) https://doi.org/10.1093/mnras/staf811
Prediction of Star Formation Rates Using an Artificial Neural Network
Ashraf Ayubinia, Jong-Hak Woo, Fatemeh Hafezianzadeh, Taehwan Kim and Changseok Kim The Astrophysical Journal 980(2) 177 (2025) https://doi.org/10.3847/1538-4357/ada366
Mass, Luminosity, and Stellar Age of Early-type Stars from the LAMOST Survey
Qida Li, Jianping Xiong, Jiao Li, Yanjun Guo, Zhanwen Han, Xuefei Chen and Chao Liu The Astrophysical Journal Supplement Series 276(1) 19 (2025) https://doi.org/10.3847/1538-4365/ad8fa9
Interpreting deep learning-based stellar mass estimation via causal analysis and mutual information decomposition
Wei Zhang, Qiufan Lin, Yuan-Sen Ting, Shupei Chen, Hengxin Ruan, Song Li and Yifan Wang Astronomy & Astrophysics 703 A276 (2025) https://doi.org/10.1051/0004-6361/202554065
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
GalaxyGenius: Mock galaxy image generator for various telescopes from hydrodynamical simulations
Xingchen Zhou, Hang Yang, Nan Li, Qi Xiong, Furen Deng, Xian-Min Meng, Renhao Ye, Shiyin Shen, Peng Wei, Qifan Cui, Zizhao He, Ayodeji Ibitoye, Chengliang Wei and Yuedong Fang Astronomy & Astrophysics 700 A120 (2025) https://doi.org/10.1051/0004-6361/202554287
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
Enhancing Cosmological Model Selection with Interpretable Machine Learning
Deep Learning-based Detection and Segmentation of Edge-on and Highly Inclined Galaxies
Ž. Chrobáková, V. Krešňáková, R. Nagy, J. Gazdová and P. Butka Publications of the Astronomical Society of the Pacific 137(3) 034101 (2025) https://doi.org/10.1088/1538-3873/adbcd6
Retrieval of the physical parameters of galaxies from WEAVE-StePS-like data using machine learning
J. Angthopo, B. R. Granett, F. La Barbera, M. Longhetti, A. Iovino, M. Fossati, F. R. Ditrani, L. Costantin, S. Zibetti, A. Gallazzi, P. Sánchez-Blázquez, C. Tortora, C. Spiniello, B. Poggianti, A. Vazdekis, M. Balcells, S. Bardelli, C. R. Benn, M. Bianconi, M. Bolzonella, G. Busarello, L. P. Cassarà, E. M. Corsini, O. Cucciati, G. Dalton, et al. Astronomy & Astrophysics 690 A198 (2024) https://doi.org/10.1051/0004-6361/202449979
Dissecting a miniature universe: A multi-wavelength view of galaxy quenching in the Shapley supercluster
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
Baisen Ma, Qi Li, Bo Qiu, Yuanlu Chen, Yajuan Zhang, Congcong Shen and Yilong Wang 52 (2024) https://doi.org/10.1109/NTCI64025.2024.10776379
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
Galaxy stellar and total mass estimation using machine learning
Jiani Chu, Hongming Tang, Dandan Xu, Shengdong Lu and Richard Long Monthly Notices of the Royal Astronomical Society 528(4) 6354 (2024) https://doi.org/10.1093/mnras/stae406
Exploring galactic properties with machine learning
Multi-layer Perceptron for Predicting Galaxy Parameters (MLP-GaP): Stellar Masses and Star Formation Rates
Xiaotong 晓通 Guo 郭, Guanwen Fang, Haicheng Feng and Rui Zhang Research in Astronomy and Astrophysics 24(12) 125019 (2024) https://doi.org/10.1088/1674-4527/ad95d7
Machine learning applications in studies of the physical properties of active galactic nuclei based on photometric observations
Estimation of stellar mass and star formation rate based on galaxy images
Jing Zhong, Zhijie Deng, Xiangru Li, Lili Wang, Haifeng Yang, Hui Li and Xirong Zhao Monthly Notices of the Royal Astronomical Society 531(1) 2011 (2024) https://doi.org/10.1093/mnras/stae1271
Deep Learning Voigt Profiles. I. Single-Cloud Doublets
Bryson Stemock, Christopher W. Churchill, Avery Lee, Sultan Hassan, Caitlin Doughty and Rogelio Ochoa The Astronomical Journal 167(6) 287 (2024) https://doi.org/10.3847/1538-3881/ad402b
Characterizing and understanding galaxies with two parameters
Suchetha Cooray, Tsutomu T Takeuchi, Daichi Kashino, Shuntaro A Yoshida, Hai-Xia Ma and Kai T Kono Monthly Notices of the Royal Astronomical Society 524(4) 4976 (2023) https://doi.org/10.1093/mnras/stad2129
Exploring supernova gravitational waves with machine learning
A Mitra, B Shukirgaliyev, Y S Abylkairov and E Abdikamalov Monthly Notices of the Royal Astronomical Society 520(2) 2473 (2023) https://doi.org/10.1093/mnras/stad169
Classifying MaNGA velocity dispersion profiles by machine learning
Improving machine learning-derived photometric redshifts and physical property estimates using unlabelled observations
A Humphrey, P A C Cunha, A Paulino-Afonso, et al. Monthly Notices of the Royal Astronomical Society 520(1) 305 (2023) https://doi.org/10.1093/mnras/stac3596
Relating the Structure of Dark Matter Halos to Their Assembly and Environment
Yangyao Chen, H. J. Mo, Cheng Li, Huiyuan Wang, Xiaohu Yang, Youcai Zhang and Kai Wang The Astrophysical Journal 899(1) 81 (2020) https://doi.org/10.3847/1538-4357/aba597
Filament profiles from WISExSCOS galaxies as probes of the impact of environmental effects
Detecting outliers in astronomical images with deep generative networks
Berta Margalef-Bentabol, Marc Huertas-Company, Tom Charnock, et al. Monthly Notices of the Royal Astronomical Society 496(2) 2346 (2020) https://doi.org/10.1093/mnras/staa1647
Predicting star formation properties of galaxies using deep learning
Shraddha Surana, Yogesh Wadadekar, Omkar Bait and Hrushikesh Bhosale Monthly Notices of the Royal Astronomical Society 493(4) 4808 (2020) https://doi.org/10.1093/mnras/staa537
Deep learning for Sunyaev–Zel’dovich detection in Planck
Star formation rates for photometric samples of galaxies using machine learning methods
M Delli Veneri, S Cavuoti, M Brescia, G Longo and G Riccio Monthly Notices of the Royal Astronomical Society 486(1) 1377 (2019) https://doi.org/10.1093/mnras/stz856