Euclid preparation

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

doi:10.1051/0004-6361/202243950

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%.

Euclid preparation / van Mierlo, S.E., Caputi, K.I., Ashby, M., Atek, H., Bolzonella, M., Bowler, R.A.A., Brammer, G., Conselice, C.J., Cuby, J., Dayal, P., D??az-S??nchez, A., Finkelstein, S.L., Hoekstra, H., Humphrey, A., Ilbert, O., Mccracken, H.J., Milvang-Jensen, B., Oesch, P.A., Pello, R., Rodighiero, G., et al.. - In: ASTRONOMY & ASTROPHYSICS. - ISSN 0004-6361. - STAMPA. - 666:(2022), pp. A200."-"-A200."-". [10.1051/0004-6361/202243950]