October
2022
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2022A&A...666A.200V
Authors
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van Mierlo, S. E.
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Caputi, K. I.
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Ashby, M.
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Atek, H.
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Bolzonella, M.
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Bowler, R. A. A.
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Brammer, G.
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Conselice, C. J.
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Cuby, J.
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Dayal, P.
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Díaz-Sánchez, A.
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Finkelstein, S. L.
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Hoekstra, H.
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Humphrey, A.
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Ilbert, O.
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McCracken, H. J.
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Milvang-Jensen, B.
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Oesch, P. A.
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Pello, R.
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Rodighiero, G.
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Schirmer, M.
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Toft, S.
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Weaver, J. R.
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Wilkins, S. M.
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Willott, C. J.
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Zamorani, G.
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Amara, A.
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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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Brescia, M.
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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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Castellano, M.
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Cavuoti, S.
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Cimatti, A.
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Cledassou, R.
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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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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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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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Galeotta, S.
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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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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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Kiessling, A.
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Kilbinger, M.
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Kitching, T.
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Kohley, R.
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Kunz, M.
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Kurki-Suonio, H.
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Laureijs, R.
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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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Pires, S.
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Poncet, M.
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Popa, L.
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Pozzetti, 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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Romelli, E.
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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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Secroun, A.
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Sirignano, C.
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Sirri, G.
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Stanco, L.
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Starck, J. -L.
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Surace, C.
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Tallada-Crespí, P.
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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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Zoubian, J.
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Andreon, S.
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Bardelli, S.
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Boucaud, A.
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Graciá-Carpio, J.
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Maino, D.
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Mauri, N.
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Mei, S.
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Sureau, F.
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Zucca, E.
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Aussel, H.
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Baccigalupi, C.
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Balaguera-Antolínez, A.
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Biviano, A.
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Blanchard, A.
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Borgani, S.
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Bozzo, E.
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Burigana, C.
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Cabanac, R.
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Calura, F.
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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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Colodro-Conde, C.
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Cooray, A. R.
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Coupon, J.
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Courtois, H. M.
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Crocce, M.
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Cucciati, O.
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Davini, S.
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Dole, H.
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Escartin, J. A.
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Escoffier, S.
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Fabricius, M.
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Farina, M.
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Ganga, K.
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García-Bellido, J.
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George, K.
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Giacomini, F.
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Gozaliasl, G.
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Gwyn, S.
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Hook, I.
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Huertas-Company, M.
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Kansal, V.
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Kashlinsky, A.
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Keihanen, E.
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Kirkpatrick, C. C.
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Lindholm, V.
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Maoli, R.
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Martinelli, M.
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Martinet, N.
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Maturi, M.
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Metcalf, R. B.
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Monaco, P.
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Morgante, G.
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Nucita, A. A.
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Patrizii, L.
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Peel, A.
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Pollack, J.
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Popa, V.
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Porciani, C.
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Potter, D.
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Reimberg, P.
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Sánchez, A. G.
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Scottez, V.
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Sefusatti, E.
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Stadel, J.
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Teyssier, R.
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Valiviita, J.
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Viel, M.
Abstract
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Context. The Euclid mission is expected to discover thousands of z > 6 galaxies in three deep fields, which together will cover a ∼50 deg2 area. However, the limited number of Euclid bands (four) and the low availability of ancillary data could make the identification of z > 6 galaxies challenging.
Aims: In this work we assess the degree of contamination by intermediate-redshift galaxies (z = 1-5.8) expected for z > 6 galaxies within the Euclid Deep Survey.
Methods: This study is based on ∼176 000 real galaxies at z = 1-8 in a ∼0.7 deg2 area selected from the UltraVISTA ultra-deep survey and ∼96 000 mock galaxies with 25.3 ≤ H < 27.0, which altogether cover the range of magnitudes to be probed in the Euclid Deep Survey. We simulate Euclid and ancillary photometry from fiducial 28-band photometry and fit spectral energy distributions to various combinations of these simulated data.
Results: We demonstrate that identifying z > 6 galaxies with Euclid data alone will be very effective, with a z > 6 recovery of 91% (88%) for bright (faint) galaxies. For the UltraVISTA-like bright sample, the percentage of z = 1-5.8 contaminants amongst apparent z > 6 galaxies as observed with Euclid alone is 18%, which is reduced to 4% (13%) by including ultra-deep Rubin (Spitzer) photometry. Conversely, for the faint mock sample, the contamination fraction with Euclid alone is considerably higher at 39%, and minimised to 7% when including ultra-deep Rubin data. For UltraVISTA-like bright galaxies, we find that Euclid (IE − YE) > 2.8 and (YE − JE) < 1.4 colour criteria can separate contaminants from true z > 6 galaxies, although these are applicable to only 54% of the contaminants as many have unconstrained (IE − YE) colours. In the best scenario, these cuts reduce the contamination fraction to 1% whilst preserving 81% of the fiducial z > 6 sample. For the faint mock sample, colour cuts are infeasible; we find instead that a 5σ detection threshold requirement in at least one of the Euclid near-infrared bands reduces the contamination fraction to 25%.
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