Euclid preparation
Authors: 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. , Díaz-Sánchez 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. , Kümmel 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-Crespí 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. , Graciá-Carpio J. , Maino D. , Mauri N. , Mei S. , Sureau F. , Zucca E. , Aussel H. , Baccigalupi C. , Balaguera-Antolínez 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. , García-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. , Sánchez A. G. , Scottez V. , Sefusatti E. , Stadel J. , Teyssier R. , Valiviita J. , Viel M. 
Published: August 2022

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%.