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Published October 2022 | Published
Journal Article Open

Euclid preparation. XXI. Intermediate-redshift contaminants in the search for z > 6 galaxies within the Euclid Deep Survey

Euclid Collaboration
van Mierlo, S. E.1 ORCID icon
Caputi, K. I.1, 2 ORCID icon
Ashby, M.3 ORCID icon
Atek, H.4 ORCID icon
Bolzonella, M.5
Bowler, R. A. A.6, 7 ORCID icon
Brammer, G.2 ORCID icon
Conselice, C. J.6 ORCID icon
Cuby, J.8
Dayal, P.1
Díaz-Sánchez, A.9 ORCID icon
Finkelstein, S. L.10
Hoekstra, H.11
Humphrey, A.12 ORCID icon
Ilbert, O.8 ORCID icon
McCracken, H. J.4, 13 ORCID icon
Milvang-Jensen, B.2 ORCID icon
Oesch, P. A.2, 14
Pello, R.8 ORCID icon
Rodighiero, G.15
Schirmer, M.16
Toft, S.2 ORCID icon
Weaver, J. R.2 ORCID icon
Wilkins, S. M.17 ORCID icon
Willott, C. J.18 ORCID icon
Zamorani, G.5 ORCID icon
Amara, A.19
Auricchio, N.5 ORCID icon
Baldi, M.20, 5, 21 ORCID icon
Bender, R.22, 23 ORCID icon
Bodendorf, C.22
Bonino, D.24 ORCID icon
Branchini, E.25, 26 ORCID icon
Brescia, M.27 ORCID icon
Brinchmann, J.12 ORCID icon
Camera, S.28, 29, 24 ORCID icon
Capobianco, V.24 ORCID icon
Carbone, C.30 ORCID icon
Carretero, J.31 ORCID icon
Castellano, M.32 ORCID icon
Cavuoti, S.27, 33, 34 ORCID icon
Cimatti, A.20, 35 ORCID icon
Cledassou, R.36, 37
Congedo, G.38 ORCID icon
Conversi, L.39, 40 ORCID icon
Copin, Y.41 ORCID icon
Corcione, L.24 ORCID icon
Courbin, F.42 ORCID icon
Da Silva, A.43 ORCID icon
Degaudenzi, H.14
Douspis, M.44 ORCID icon
Dubath, F.14
Dupac, X.40
Dusini, S.45
Farrens, S.46 ORCID icon
Ferriol, S.41
Frailis, M.47 ORCID icon
Franceschi, E.5 ORCID icon
Franzetti, P.30 ORCID icon
Fumana, M.30 ORCID icon
Galeotta, S.47 ORCID icon
Garilli, B.30 ORCID icon
Gillard, W.48 ORCID icon
Gillis, B.38 ORCID icon
Giocoli, C.49, 21 ORCID icon
Grazian, A.50
Grupp, F.22, 23 ORCID icon
Haugan, S. V. H.51
Holmes, W.52
Hormuth, F.
Hornstrup, A.53 ORCID icon
Jahnke, K.16 ORCID icon
Kümmel, M.23
Kiessling, A.52 ORCID icon
Kilbinger, M.54 ORCID icon
Kitching, T.55
Kohley, R.40
Kunz, M.14 ORCID icon
Kurki-Suonio, H.56 ORCID icon
Laureijs, R.57
Ligori, S.24 ORCID icon
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Lloro, I.58
Maiorano, E.5 ORCID icon
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Marggraf, O.59
Markovic, K.52 ORCID icon
Marulli, F.20, 5, 21 ORCID icon
Massey, R.60 ORCID icon
Maurogordato, S.61
Medinaceli, E.49
Meneghetti, M.5, 21 ORCID icon
Merlin, E.32 ORCID icon
Meylan, G.42
Moresco, M.20, 5 ORCID icon
Moscardini, L.20, 5, 21 ORCID icon
Munari, E.47 ORCID icon
Niemi, S. M.57
Padilla, C.31 ORCID icon
Paltani, S.14
Pasian, F.47 ORCID icon
Pedersen, K.62
Pettorino, V.54 ORCID icon
Pires, S.46 ORCID icon
Poncet, M.36
Popa, L.63 ORCID icon
Pozzetti, L.5 ORCID icon
Raison, F.22
Renzi, A.15, 45 ORCID icon
Rhodes, J.52 ORCID icon
Riccio, G.27 ORCID icon
Romelli, E.47 ORCID icon
Rossetti, E.20 ORCID icon
Saglia, R.22, 23 ORCID icon
Sapone, D.64
Sartoris, B.65, 47 ORCID icon
Schneider, P.59
Secroun, A.48 ORCID icon
Sirignano, C.15, 45 ORCID icon
Sirri, G.21 ORCID icon
Stanco, L.45
Starck, J.-L.46 ORCID icon
Surace, C.8 ORCID icon
Tallada-Crespí, P.66 ORCID icon
Taylor, A. N.38
Tereno, I.43 ORCID icon
Toledo-Moreo, R.9 ORCID icon
Torradeflot, F.66 ORCID icon
Tutusaus, I.14 ORCID icon
Valentijn, E. A.1 ORCID icon
Valenziano, L.5, 21 ORCID icon
Vassallo, T.23 ORCID icon
Wang, Y.67, 68 ORCID icon
Zacchei, A.47 ORCID icon
Zoubian, J.48
Andreon, S.69 ORCID icon
Bardelli, S.5 ORCID icon
Boucaud, A.70 ORCID icon
Graciá-Carpio, J.22 ORCID icon
Maino, D.71, 30, 72 ORCID icon
Mauri, N.20, 21 ORCID icon
Mei, S.70
Sureau, F.46 ORCID icon
Zucca, E.5 ORCID icon
Aussel, H.46 ORCID icon
Baccigalupi, C.65, 47, 73, 74
Balaguera-Antolínez, A.75, 76 ORCID icon
Biviano, A.47, 65 ORCID icon
Blanchard, A.77
Borgani, S.47, 65, 73, 78 ORCID icon
Bozzo, E.14
Burigana, C.79, 49, 21 ORCID icon
Cabanac, R.77
Calura, F.5 ORCID icon
Cappi, A.61, 5 ORCID icon
Carvalho, C. S.43 ORCID icon
Casas, S.80 ORCID icon
Castignani, G.20, 5 ORCID icon
Colodro-Conde, C.75
Cooray, A. R.81 ORCID icon
Coupon, J.14
Courtois, H. M.41
Crocce, M.82 ORCID icon
Cucciati, O.5
Davini, S.83
Dole, H.44
Escartin, J. A.22
Escoffier, S.48
Fabricius, M.22
Farina, M.84 ORCID icon
Ganga, K.70
García-Bellido, J.85 ORCID icon
George, K.23
Giacomini, F.21 ORCID icon
Gozaliasl, G.56 ORCID icon
Gwyn, S.18
Hook, I.86 ORCID icon
Huertas-Company, M.54, 87, 75 ORCID icon
Kansal, V.46 ORCID icon
Kashlinsky, A.88, 89
Keihanen, E.56 ORCID icon
Kirkpatrick, C. C.56
Lindholm, V.56 ORCID icon
Maoli, R.90, 32 ORCID icon
Martinelli, M.32 ORCID icon
Martinet, N.8 ORCID icon
Maturi, M.91
Metcalf, R. B.20, 5 ORCID icon
Monaco, P.78, 65, 47, 73
Morgante, G.5 ORCID icon
Nucita, A. A.92, 93, 94 ORCID icon
Patrizii, L.21
Peel, A.42
Pollack, J.70
Popa, V.63
Porciani, C.59
Potter, D.95 ORCID icon
Reimberg, P.4 ORCID icon
Sánchez, A. G.22
Scottez, V.4
Sefusatti, E.47, 65, 73 ORCID icon
Stadel, J.95 ORCID icon
Teyssier, R.96
Valiviita, J.97, 98 ORCID icon
Viel, M.47, 65, 74, 73
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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%.

Copyright and License

© S. E. van Mierlo et al. 2022.

Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Acknowledgement

Based on data products from observations conducted with ESO Telescopes at the Paranal Observatory under ESO program ID 179.A-2005 and on data products produced by TERAPIX and the Cambridge Astronomy Survey Unit on behalf of the UltraVISTA consortium. Also based in part on observations carried out with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. Also based on observations carried out by NASA/ESA Hubble Space Telescope, obtained and archived at the Space Telescope Science Institute; and the Subaru Telescope, which is operated by the National Astronomical Observatory of Japan. This research has made use of the NASA/IPAC Infrared Science Archive, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. SvM and KC acknowledge funding from the European Research Council through the award of the Consolidator Grant ID 681627-BUILDUP. PD acknowledges support from the European Research Council’s starting grant ERC StG-717001 (DELPHI), from the NWO grant 016.VIDI.189.162 (ODIN) and the European Commission’s and University of Groningen’s CO-FUND Rosalind Franklin program. The Euclid Consortium acknowledges the European Space Agency and a number of agencies and institutes that have supported the development of Euclid, in particular the Academy of Finland, the Agenzia Spaziale Italiana, the Belgian Science Policy, the Canadian Euclid Consortium, the French Centre National d’Etudes Spatiales, the Deutsches Zentrum für Luft- und Raumfahrt, the Danish Space Research Institute, the Fundação para a Ciência e a Tecnologia, the Ministerio de Economia y Competitividad, the National Aeronautics and Space Administration, the National Astronomical Observatory of Japan, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Romanian Space Agency, the State Secretariat for Education, Research and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid web site (http://www.euclid-ec.org). We thank Smaran Deshmukh for useful discussions on the SMUVS catalogue photometry. We thank Marc Sauvage for carefully reading the manuscript and providing constructive comments for the Euclid Consortium internal review.

Funding

SvM and KC acknowledge funding from the European Research Council through the award of the Consolidator Grant ID 681627-BUILDUP. PD acknowledges support from the European Research Council’s starting grant ERC StG-717001 (DELPHI), from the NWO grant 016.VIDI.189.162 (ODIN) and the European Commission’s and University of Groningen’s CO-FUND Rosalind Franklin program.

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Additional details

Created:
November 19, 2024
Modified:
November 19, 2024