April
2023
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2023MNRAS.520.3529E
Authors
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Euclid Collaboration
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Bisigello, L.
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Conselice, C. J.
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Baes, M.
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Bolzonella, M.
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Brescia, M.
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Cavuoti, S.
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Cucciati, O.
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Humphrey, A.
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Hunt, L. K.
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Maraston, C.
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Pozzetti, L.
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Tortora, C.
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van Mierlo, S. E.
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Aghanim, N.
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Auricchio, N.
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Baldi, M.
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Bender, R.
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Bodendorf, C.
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Bonino, D.
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Branchini, E.
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Brinchmann, J.
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Camera, S.
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Capobianco, V.
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Carbone, C.
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Carretero, J.
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Castander, F. J.
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Castellano, M.
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Cimatti, A.
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Congedo, G.
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Conversi, L.
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Copin, Y.
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Corcione, L.
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Courbin, F.
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Cropper, M.
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Da Silva, A.
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Degaudenzi, H.
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Douspis, M.
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Dubath, F.
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Duncan, C. A. J.
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Dupac, X.
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Dusini, S.
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Farrens, S.
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Ferriol, S.
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Frailis, M.
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Franceschi, E.
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Franzetti, P.
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Fumana, M.
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Garilli, B.
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Gillard, W.
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Gillis, B.
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Giocoli, C.
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Grazian, A.
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Grupp, F.
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Guzzo, L.
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Haugan, S. V. H.
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Holmes, W.
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Hormuth, F.
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Hornstrup, A.
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Jahnke, K.
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Kümmel, M.
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Kermiche, S.
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Kiessling, A.
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Kilbinger, M.
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Kohley, R.
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Kunz, M.
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Kurki-Suonio, H.
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Ligori, S.
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Lilje, P. B.
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Lloro, I.
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Maiorano, E.
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Mansutti, O.
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Marggraf, O.
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Markovic, K.
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Marulli, F.
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Massey, R.
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Maurogordato, S.
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Medinaceli, E.
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Meneghetti, M.
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Merlin, E.
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Meylan, G.
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Moresco, M.
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Moscardini, L.
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Munari, E.
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Niemi, S. M.
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Padilla, C.
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Paltani, S.
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Pasian, F.
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Pedersen, K.
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Pettorino, V.
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Polenta, G.
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Poncet, M.
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Popa, L.
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Raison, F.
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Renzi, A.
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Rhodes, J.
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Riccio, G.
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Rix, H. -W.
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Romelli, E.
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Roncarelli, M.
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Rosset, C.
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Rossetti, E.
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Saglia, R.
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Sapone, D.
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Sartoris, B.
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Schneider, P.
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Scodeggio, M.
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Secroun, A.
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Seidel, G.
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Sirignano, C.
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Sirri, G.
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Stanco, L.
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Tallada-Crespí, P.
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Tavagnacco, D.
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Taylor, A. N.
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Tereno, I.
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Toledo-Moreo, R.
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Torradeflot, F.
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Tutusaus, I.
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Valentijn, E. A.
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Valenziano, L.
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Vassallo, T.
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Wang, Y.
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Zacchei, A.
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Zamorani, G.
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Zoubian, J.
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Andreon, S.
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Bardelli, S.
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Boucaud, A.
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Colodro-Conde, C.
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Di Ferdinando, D.
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Graciá-Carpio, J.
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Lindholm, V.
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Maino, D.
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Mei, S.
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Scottez, V.
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Sureau, F.
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Tenti, M.
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Zucca, E.
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Borlaff, A. S.
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Ballardini, M.
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Biviano, A.
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Bozzo, E.
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Burigana, C.
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Cabanac, R.
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Cappi, A.
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Carvalho, C. S.
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Casas, S.
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Castignani, G.
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Cooray, A.
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Coupon, J.
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Courtois, H. M.
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Cuby, J.
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Davini, S.
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De Lucia, G.
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Desprez, G.
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Dole, H.
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Escartin, J. A.
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Escoffier, S.
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Farina, M.
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Fotopoulou, S.
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Ganga, K.
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Garcia-Bellido, J.
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George, K.
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Giacomini, F.
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Gozaliasl, G.
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Hildebrandt, H.
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Hook, I.
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Huertas-Company, M.
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Kansal, V.
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Keihanen, E.
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Kirkpatrick, C. C.
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Loureiro, A.
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Macías-Pérez, J. F.
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Magliocchetti, M.
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Mainetti, G.
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Marcin, S.
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Martinelli, M.
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Martinet, N.
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Metcalf, R. B.
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Monaco, P.
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Morgante, G.
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Nadathur, S.
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Nucita, A. A.
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Patrizii, L.
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Peel, A.
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Potter, D.
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Pourtsidou, A.
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Pöntinen, M.
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Reimberg, P.
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Sánchez, A. G.
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Sakr, Z.
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Schirmer, M.
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Sefusatti, E.
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Sereno, M.
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Stadel, J.
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Teyssier, R.
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Valieri, C.
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Valiviita, J.
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Viel, M.
Abstract
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Next-generation telescopes, like Euclid, Rubin/LSST, and Roman, will open new windows on the Universe, allowing us to infer physical properties for tens of millions of galaxies. Machine-learning methods are increasingly becoming the most efficient tools to handle this enormous amount of data, because they are often faster and more accurate than traditional methods. We investigate how well redshifts, stellar masses, and star-formation rates (SFRs) can be measured with deep-learning algorithms for observed galaxies within data mimicking the Euclid and Rubin/LSST surveys. We find that deep-learning neural networks and convolutional neural networks (CNNs), which are dependent on the parameter space of the training sample, perform well in measuring the properties of these galaxies and have a better accuracy than methods based on spectral energy distribution fitting. CNNs allow the processing of multiband magnitudes together with $H_{\scriptscriptstyle \rm E}$-band images. We find that the estimates of stellar masses improve with the use of an image, but those of redshift and SFR do not. Our best results are deriving (i) the redshift within a normalized error of <0.15 for 99.9 ${{\ \rm per\ cent}}$ of the galaxies with signal-to-noise ratio >3 in the $H_{\scriptscriptstyle \rm E}$ band; (ii) the stellar mass within a factor of two ($\sim\!0.3 \rm \ dex$) for 99.5 ${{\ \rm per\ cent}}$ of the considered galaxies; and (iii) the SFR within a factor of two ($\sim\!0.3 \rm \ dex$) for $\sim\!70{{\ \rm per\ cent}}$ of the sample. We discuss the implications of our work for application to surveys as well as how measurements of these galaxy parameters can be improved with deep learning.
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