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).
This article has been cited by the following article(s):
Beyond traditional diagnostics: Identifying active galactic nuclei using spectral energy distribution fitting in DESI data
M. Siudek, M. Mezcua, C. Circosta, C. Maraston, J. Moustakas, H. Zou, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, K. S. Dawson, A. de la Macorra, A. Dey, P. Doel, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, M. Ishak, S. Juneau, D. Kirkby, T. Kisner, A. Kremin, A. Lambert, et al. Astronomy & Astrophysics 700 A209 (2025) https://doi.org/10.1051/0004-6361/202555463
The quiescent population at 0.5 ≤ z ≤ 0.9: Environmental impact on the mass–size relation
M. Figueira, M. Siudek, A. Pollo, J. Krywult, D. Vergani, M. Bolzonella, O. Cucciati and A. Iovino Astronomy & Astrophysics 687 A117 (2024) https://doi.org/10.1051/0004-6361/202347774
Spectral similarities in galaxies through an unsupervised classification of spaxels
Value-added catalog of physical properties for more than 1.3 million galaxies from the DESI survey
M. Siudek, R. Pucha, M. Mezcua, S. Juneau, J. Aguilar, S. Ahlen, D. Brooks, C. Circosta, T. Claybaugh, S. Cole, K. Dawson, A. de la Macorra, A. Dey, B. Dey, P. Doel, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, C. Howlett, M. Ishak, R. Kehoe, D. Kirkby, et al. Astronomy & Astrophysics 691 A308 (2024) https://doi.org/10.1051/0004-6361/202451761
Attenuation proxy hidden in surface brightness – colour diagrams
K. Małek, Junais, A. Pollo, M. Boquien, V. Buat, S. Salim, S. Brough, R. Demarco, A. W. Graham, M. Hamed, J. R. Mullaney, M. Romano, C. Sifón, M. Aravena, J. A. Benavides, I. Busà, D. Donevski, O. Dorey, H. M. Hernandez-Toledo, A. Nanni, W. J. Pearson, F. Pistis, R. Ragusa, G. Riccio and J. Román Astronomy & Astrophysics 684 A30 (2024) https://doi.org/10.1051/0004-6361/202348432
From VIPERS to SDSS: Unveiling galaxy spectra evolution over 9 Gyr through unsupervised machine learning
Wide Area VISTA Extra-galactic Survey (WAVES): unsupervised star-galaxy separation on the WAVES-Wide photometric input catalogue using UMAP and hdbscan
Todd L Cook, Behnood Bandi, Sam Philipsborn, Jon Loveday, Sabine Bellstedt, Simon P Driver, Aaron S G Robotham, Maciej Bilicki, Gursharanjit Kaur, Elmo Tempel, Ivan Baldry, Daniel Gruen, Marcella Longhetti, Angela Iovino, Benne W Holwerda and Ricardo Demarco Monthly Notices of the Royal Astronomical Society 535(3) 2129 (2024) https://doi.org/10.1093/mnras/stae2389
J. Dubois, D. Fraix-Burnet and J. Moultaka 60 67 (2023) https://doi.org/10.1007/978-3-031-34167-0_14
The first catalogue of spectroscopically confirmed red nuggets at z ∼ 0.7 from the VIPERS survey
Environments of red nuggets at z ∼ 0.7 from the VIPERS survey
M Siudek, K Lisiecki, J Krywult, D Donevski, C P Haines, A Karska, K Małek, T Moutard and A Pollo Monthly Notices of the Royal Astronomical Society 523(3) 4294 (2023) https://doi.org/10.1093/mnras/stad1685
Influence of star-forming galaxy selection on the galaxy main sequence
The PAU survey: classifying low-z SEDs using Machine Learning clustering
A L González-Morán, P Arrabal Haro, C Muñoz-Tuñón, J M Rodríguez-Espinosa, J Sánchez-Almeida, J Calhau, E Gaztañaga, F J Castander, P Renard, L Cabayol, E Fernandez, C Padilla, J Garcia-Bellido, R Miquel, J De Vicente, E Sanchez, I Sevilla-Noarbe and D Navarro-Gironés Monthly Notices of the Royal Astronomical Society 524(3) 3569 (2023) https://doi.org/10.1093/mnras/stad2123
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
In pursuit of giants
D. Donevski, I. Damjanov, A. Nanni, A. Man, M. Giulietti, M. Romano, A. Lapi, D. Narayanan, R. Davé, I. Shivaei, J. Sohn, Junais, L. Pantoni and Q. Li Astronomy & Astrophysics 678 A35 (2023) https://doi.org/10.1051/0004-6361/202346066
M. Siudek, K. Lisiecki, M. Mezcua, K. Małek, A. Pollo, J. Krywult, A. Karska and M. Junais 60 71 (2023) https://doi.org/10.1007/978-3-031-34167-0_15
Across the green valley with HST grisms: colour evolution, crossing time-scales, and the growth of the red sequence at z = 1.0–1.8
Gaël Noirot, Marcin Sawicki, Roberto Abraham, Maruša Bradač, Kartheik Iyer, Thibaud Moutard, Camilla Pacifici, Swara Ravindranath and Chris J Willott Monthly Notices of the Royal Astronomical Society 512(3) 3566 (2022) https://doi.org/10.1093/mnras/stac668
COSMOS2020: Manifold learning to estimate physical parameters in large galaxy surveys
The PAU survey: measurements of the 4000 Å spectral break with narrow-band photometry
Pablo Renard, Malgorzata Siudek, Martin B Eriksen, et al. Monthly Notices of the Royal Astronomical Society 515(1) 146 (2022) https://doi.org/10.1093/mnras/stac1730
Shaping physical properties of galaxy subtypes in the VIPERS survey: Environment matters
Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks
T-Y Cheng, H Domínguez Sánchez, J Vega-Ferrero, et al. Monthly Notices of the Royal Astronomical Society 518(2) 2794 (2022) https://doi.org/10.1093/mnras/stac3228
Pushing automated morphological classifications to their limits with the Dark Energy Survey
J Vega-Ferrero, H Domínguez Sánchez, M Bernardi, et al. Monthly Notices of the Royal Astronomical Society 506(2) 1927 (2021) https://doi.org/10.1093/mnras/stab594
Beyond the hubble sequence – exploring galaxy morphology with unsupervised machine learning
Ting-Yun Cheng, Marc Huertas-Company, Christopher J Conselice, et al. Monthly Notices of the Royal Astronomical Society 503(3) 4446 (2021) https://doi.org/10.1093/mnras/stab734
The PAU Survey: Intrinsic alignments and clustering of narrow-band photometric galaxies
Synergies between low- and intermediate-redshift galaxy populations revealed with unsupervised machine learning
Sebastian Turner, Malgorzata Siudek, Samir Salim, et al. Monthly Notices of the Royal Astronomical Society 503(2) 3010 (2021) https://doi.org/10.1093/mnras/stab653
Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks
Ting-Yun Cheng, Christopher J Conselice, Alfonso Aragón-Salamanca, et al. Monthly Notices of the Royal Astronomical Society 507(3) 4425 (2021) https://doi.org/10.1093/mnras/stab2142
Unsupervised classification of SDSS galaxy spectra
Deep extragalactic visible legacy survey (DEVILS): stellar mass growth by morphological type since z = 1
Abdolhosein Hashemizadeh, Simon P Driver, Luke J M Davies, et al. Monthly Notices of the Royal Astronomical Society 505(1) 136 (2021) https://doi.org/10.1093/mnras/stab600
A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning
Charles L. Steinhardt, John R. Weaver, Jack Maxfield, Iary Davidzon, Andreas L. Faisst, Dan Masters, Madeline Schemel and Sune Toft The Astrophysical Journal 891(2) 136 (2020) https://doi.org/10.3847/1538-4357/ab76be
Galaxy morphological classification in deep-wide surveys via unsupervised machine learning
G Martin, S Kaviraj, A Hocking, S C Read and J E Geach Monthly Notices of the Royal Astronomical Society 491(1) 1408 (2020) https://doi.org/10.1093/mnras/stz3006
Identifying strong lenses with unsupervised machine learning using convolutional autoencoder
Robert B Metcalf, Simon Dye, Alfonso Aragón-Salamanca, et al. Monthly Notices of the Royal Astronomical Society 494(3) 3750 (2020) https://doi.org/10.1093/mnras/staa1015
On the slow quenching of ℳ* galaxies: heavily obscured AGNs clarify the picture
Shruti Tripathi, Stéphane Arnouts, Marcin Sawicki, Nicola Malavasi and Thibaud Moutard Monthly Notices of the Royal Astronomical Society 495(4) 4237 (2020) https://doi.org/10.1093/mnras/staa1434
Automatic classification of sources in large astronomical catalogs
Agnieszka Pollo, Aleksandra Solarz, Małgorzata Siudek, et al. Proceedings of the International Astronomical Union 15(S341) 109 (2019) https://doi.org/10.1017/S1743921319002576
How to Find Variable Active Galactic Nuclei with Machine Learning