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Identifying type II quasars at intermediate redshift with few-shot learning photometric classification,
Astronomy & Astrophysics, 687, 20
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F. Zhong, N. R. Napolitano, C. Heneka, R. Zambelli, F. E. Bauer, N. Bouche, J. Comparat, Y. Kim, J. Krogager, M. Longhetti et al. (including: A. Humphrey), 2024,
Galaxy Spectra neural Network (GaSNet). II. Using deep learning for spectral classification and redshift predictions,
Monthly Notices of the Royal Astronomical Society, 532, 22
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W. Wang, D. Wylezalek, J. Vernet, C. de Breuck, B. Gullberg, A. M. Swinbank, M. Villar Martín, M. D. Lehnert, G. Drouart, F. Arrigoni-Battaia et al. (including: A. Humphrey, P. Lagos), 2023,
3D tomography of the giant Lyα nebulae of z≈3–5 radio-loud AGN,
Astronomy & Astrophysics, 680, 44
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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 et al. (including: H. Miranda, A. Paulino-Afonso, C. Pappalardo), 2023,
Selection of powerful radio galaxies with machine learning,
Astronomy & Astrophysics, 679, 24
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Euclid Collaboration, L. Bisigello, C. J. Conselice, M. Baes, M. Bolzonella, M. Brescia, S. Cavuoti, O. Cucciati, A. Humphrey, L. K. Hunt et al. (including: J. Brinchmann, A. C. da Silva, I. Tereno, C. S. Carvalho), 2023,
Euclid preparation – XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images,
Monthly Notices of the Royal Astronomical Society, 520, 19
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L. Binette, Y. Krongold, S. A. R. Haro-Corzo, A. Humphrey, S. G. Morais, 2023,
Optimized Spectral Energy Distribution for Seyfert Galaxies,
Revista Mexicana de Astronomía y Astrofísica, 53, 9
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