S. E. van Mierlo, K. Caputi, M. L. N. Ashby, H. Atek, M. Bolzonella, R. A. A. Bowler, G. Brammer, C. J. Conselice, J.-G. Cuby, P. Dayal, A. Díaz-Sánchez, S. L. Finkelstein, H. Hoekstra, A. Humphrey, O. Ilbert, H. J. McCracken, B. Milvang-Jensen, P. Oesch, R. Pello, G. Rodighiero, M. Schirmer, S. Toft, J. R. Weaver, S. M. Wilkins, C. J. Willott, G. Zamorani, A. Amara, N. Auricchio, M. Baldi, R. Bender, C. Bodendorf, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, M. Castellano, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, L. Conversi, Y. Copin, L. Corcione, F. Courbin, A. C. da Silva, H. Degaudenzi, M. Douspis, F. Dubath, X. Dupac, S. Dusini, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, P. Franzetti, M. Fumana, S. Galeotta, B. Garilli, W. Gillard, B. R. Gillis, C. Giocoli, A. Grazian, F. Grupp, S. V. H. Haugan, W. A. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Kümmel, A. Kiessling, M. Kilbinger, T. D. Kitching, R. Kohley, M. Kunz, H. Kurki-Suonio, R. J. Laureijs, S. Ligori, P. B. Lilje, I. Lloro, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, F. Marulli, R. Massey, S. Maurogordato, E. Medinaceli, M. Meneghetti, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, E. Munari, S. M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. D. Rhodes, G. Riccio, E. Romelli, E. Rossetti, R. Saglia, D. Sapone, B. Sartoris, P. Schneider, A. Secroun, C. Sirignano, G. Sirri, L. Stanco, J.-L. Starck, C. Surace, P. Tallada-Crespí, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, E. A. Valentijn, L. Valenziano, T. Vassallo, Y. Wang, A. Zacchei, J. Zoubian, S. Andreon, S. Bardelli, A. Boucaud, J. Graciá-Carpio, D. Maino, N. Mauri, S. Mei, F. Sureau, E. Zucca, H. Aussel, C. Baccigalupi, A. Balaguera-Antolínez, A. Biviano, A. Blanchard, S. Borgani, E. Bozzo, C. Burigana, R. Cabanac, F. Calura, A. Cappi, C. S. Carvalho, S. Casas, G. Castignani, C. Colodro-Conde, A. R. Cooray, J. Coupon, H. M. Courtois, M. Crocce, O. Cucciati, S. Davini, H. Dole, J. A. Escartin, S. Escoffier, M. Fabricius, M. Farina, K. Ganga, J. García-Bellido, K. George, F. Giacomini, G. Gozaliasl, S. D. J. Gwyn, I. M. Hook, M. Huertas-Company, V. Kansal, A. Kashlinsky, E. Keihanen, C. C. Kirkpatrick, V. Lindholm, R. Maoli, M. Martinelli, N. Martinet, M. Maturi, R. B. Metcalf, P. Monaco, G. Morgante, A. A. Nucita, L. Patrizii, A. Peel, J. E. Pollack, V. Popa, C. Porciani, D. Potter, P. Flose-Reimberg, A. G. Sánchez, V. Scottez, E. Sefusatti, J. Stadel, R. Teyssier, J. Valiviita, M. Viel

Abstract
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 (I_E − Y_E) > 2.8 and (Y_E − J_E) < 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 (I_E − Y_E) 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%.

Keywords
galaxies: high-redshift / galaxies: evolution / galaxies: photometry

Notes
This article has an erratum: [https://doi.org/10.1051/0004-6361/202243950e]

Astronomy & Astrophysics
Volume 666, Article Number A200, Number of pages 27
2022 October

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