Return of the features - Efficient feature selection and interpretation for photometric redshifts
A&A, 616 (2018) A97
Published online: 2018-08-28
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):
CIRCLEZ : Reliable photometric redshifts for active galactic nuclei computed solely using photometry from Legacy Survey Imaging for DESI
A. Saxena, M. Salvato, W. Roster, R. Shirley, J. Buchner, J. Wolf, C. Kohl, H. Starck, T. Dwelly, J. Comparat, A. Malyali, S. Krippendorf, A. Zenteno, D. Lang, D. Schlegel, R. Zhou, A. Dey, F. Valdes, A. Myers, R. J. Assef, C. Ricci, M. J. Temple, A. Merloni, A. Koekemoer, S. F. Anderson, et al.Astronomy & Astrophysics 690 A365 (2024)
https://doi.org/10.1051/0004-6361/202450886
Sustainable groundwater management using stacked LSTM with deep neural network
Eatedal Alabdulkreem, Nuha Alruwais, Hany Mahgoub, Ashit Kumar Dutta, Majdi Khalid, Radwa Marzouk, Abdelwahed Motwakel and Suhanda DrarUrban Climate 49 101469 (2023)
https://doi.org/10.1016/j.uclim.2023.101469
Photometric redshifts from SDSS images with an interpretable deep capsule network
Biprateep Dey, Brett H Andrews, Jeffrey A Newman, Yao-Yuan Mao, Markus Michael Rau and Rongpu ZhouMonthly Notices of the Royal Astronomical Society 515 (4) 5285 (2022)
https://doi.org/10.1093/mnras/stac2105
Machine learning technique for morphological classification of galaxies from the SDSS. III. The CNN image-based inference of detailed features
V. KHRAMTSOV, I. B. VAVILOVA, D. V. DOBRYCHEVA, et al.Kosmìčna nauka ì tehnologìâ 28 (5) 27 (2022)
https://doi.org/10.15407/knit2022.05.027
Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02
I. B. VAVILOVA, V. KHRAMTSOV, D. V. DOBRYCHEVA, et al.
Kosmìčna nauka ì tehnologìâ 28 (1) 03 (2022)
https://doi.org/10.15407/knit2022.01.003
Kosmìčna nauka ì tehnologìâ 28 (1) 03 (2022)
https://doi.org/10.15407/knit2022.01.003
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
Galaxy morphoto-Z with neural Networks (GaZNets)
Rui Li, Nicola R. Napolitano, Haicheng Feng, et al.Astronomy & Astrophysics 666 A85 (2022)
https://doi.org/10.1051/0004-6361/202244081
A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications
Hakan Başağaoğlu, Debaditya Chakraborty, Cesar Do Lago, Lilianna Gutierrez, Mehmet Arif Şahinli, Marcio Giacomoni, Chad Furl, Ali Mirchi, Daniel Moriasi and Sema Sevinç ŞengörWater 14 (8) 1230 (2022)
https://doi.org/10.3390/w14081230
Intelligent Astrophysics
Valeria Amaro, Stefano Cavuoti, Massimo Brescia, et al.Emergence, Complexity and Computation, Intelligent Astrophysics 39 245 (2021)
https://doi.org/10.1007/978-3-030-65867-0_11
Improving the reliability of photometric redshift with machine learning
Oleksandra Razim, Stefano Cavuoti, Massimo Brescia, et al.Monthly Notices of the Royal Astronomical Society 507 (4) 5034 (2021)
https://doi.org/10.1093/mnras/stab2334
Estimation of Photometric Redshifts. I. Machine-learning Inference for Pan-STARRS1 Galaxies Using Neural Networks
Joongoo Lee and Min-Su ShinThe Astronomical Journal 162 (6) 297 (2021)
https://doi.org/10.3847/1538-3881/ac2e96
Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4
S. J. Nakoneczny, M. Bilicki, A. Pollo, et al.Astronomy & Astrophysics 649 A81 (2021)
https://doi.org/10.1051/0004-6361/202039684
Intelligent Astrophysics
Maurizio D’Addona, Giuseppe Riccio, Stefano Cavuoti, Crescenzo Tortora and Massimo BresciaEmergence, Complexity and Computation, Intelligent Astrophysics 39 225 (2021)
https://doi.org/10.1007/978-3-030-65867-0_10
The PAU Survey: narrow-band photometric redshifts using Gaussian processes
John Y H Soo, Benjamin Joachimi, Martin Eriksen, et al.Monthly Notices of the Royal Astronomical Society 503 (3) 4118 (2021)
https://doi.org/10.1093/mnras/stab711
Exploring New Redshift Indicators for Radio-Powerful AGN
Rodrigo Carvajal, Israel Matute, José Afonso, Stergios Amarantidis, Davi Barbosa, Pedro Cunha and Andrew HumphreyGalaxies 9 (4) 86 (2021)
https://doi.org/10.3390/galaxies9040086
Photometric Redshifts With Machine Learning, Lights and Shadows on a Complex Data Science Use Case
Massimo Brescia, Stefano Cavuoti, Oleksandra Razim, et al.Frontiers in Astronomy and Space Sciences 8 (2021)
https://doi.org/10.3389/fspas.2021.658229
Galaxy morphological classification in deep-wide surveys via unsupervised machine learning
G Martin, S Kaviraj, A Hocking, S C Read and J E GeachMonthly Notices of the Royal Astronomical Society 491 (1) 1408 (2020)
https://doi.org/10.1093/mnras/stz3006
Photometric redshift estimation using ExtraTreesRegressor: Galaxies and quasars from low to very high redshifts
Moonzarin Reza and Mohammad Ariful HaqueAstrophysics and Space Science 365 (3) (2020)
https://doi.org/10.1007/s10509-020-03758-w
Groundwater Prediction Using Machine-Learning Tools
Eslam A. Hussein, Christopher Thron, Mehrdad Ghaziasgar, Antoine Bagula and Mattia VaccariAlgorithms 13 (11) 300 (2020)
https://doi.org/10.3390/a13110300
Three-dimensional episodic model of star formation in galaxies in the presence of dissipation
Sailajananda Mukherjee, Tanuka Chattopadhyay and Sukanta DasMonthly Notices of the Royal Astronomical Society 494 (3) 4098 (2020)
https://doi.org/10.1093/mnras/staa829
Grand Challenges in Astrostatistics
Didier Fraix-BurnetFrontiers in Astronomy and Space Sciences 7 (2020)
https://doi.org/10.3389/fspas.2020.00011
KiDS-SQuaD
Vladislav Khramtsov, Alexey Sergeyev, Chiara Spiniello, et al.Astronomy & Astrophysics 632 A56 (2019)
https://doi.org/10.1051/0004-6361/201936006
A Comparison of Photometric Redshift Techniques for Large Radio Surveys
Ray P. Norris, M. Salvato, G. Longo, et al.Publications of the Astronomical Society of the Pacific 131 (1004) 108004 (2019)
https://doi.org/10.1088/1538-3873/ab0f7b
Probabilistic Random Forest: A Machine Learning Algorithm for Noisy Data Sets
Itamar Reis, Dalya Baron and Sahar ShahafThe Astronomical Journal 157 (1) 16 (2019)
https://doi.org/10.3847/1538-3881/aaf101
Morpho-photometric redshifts
Kristen MenouMonthly Notices of the Royal Astronomical Society 489 (4) 4802 (2019)
https://doi.org/10.1093/mnras/stz2477
Photometric redshifts for X-ray-selected active galactic nuclei in the eROSITA era
M Brescia, M Salvato, S Cavuoti, et al.Monthly Notices of the Royal Astronomical Society 489 (1) 663 (2019)
https://doi.org/10.1093/mnras/stz2159
Foreword to the Focus Issue on Machine Intelligence in Astronomy and Astrophysics
Giuseppe Longo, Erzsébet Merényi and Peter TiňoPublications of the Astronomical Society of the Pacific 131 (1004) 100101 (2019)
https://doi.org/10.1088/1538-3873/ab2743
Unsupervised Classification of Galaxies. I. Independent Component Analysis Feature Selection
Tanuka Chattopadhyay, Didier Fraix-Burnet and Saptarshi MondalPublications of the Astronomical Society of the Pacific 131 (1004) 108010 (2019)
https://doi.org/10.1088/1538-3873/aaf7c6
Data Analytics and Management in Data Intensive Domains
Massimo Brescia, Stefano Cavuoti, Valeria Amaro, et al.Communications in Computer and Information Science, Data Analytics and Management in Data Intensive Domains 822 61 (2018)
https://doi.org/10.1007/978-3-319-96553-6_5