Euclid: Calibrating photometric redshifts with spectroscopic cross-correlations
Creators: name: Naidoo, K.
name: Johnston, H.
name: Joachimi, B.
name: Busch, J. L. van den
name: Hildebrandt, H.
name: Ilbert, O.
name: Lahav, O.
name: Aghanim, N.
name: Altieri, B.
name: Amara, A.
name: Baldi, M.
name: Bender, R.
name: Bodendorf, C.
name: Branchini, E.
name: Brescia, M.
name: Brinchmann, J.
name: Camera, S.
name: Capobianco, V.
name: Carbone, C.
name: Carretero, J.
name: Castander, F. J.
name: Castellano, M.
name: Cavuoti, S.
name: Cimatti, A.
name: Cledassou, R.
name: Congedo, G.
name: Conselice, C. J.
name: Conversi, L.
name: Copin, Y.
name: Corcione, L.
name: Courbin, F.
name: Cropper, M.
name: Da Silva, A.
name: Degaudenzi, H.
name: Dinis, J.
name: Dubath, F.
name: Dupac, X.
name: Dusini, S.
name: Farrens, S.
name: Ferriol, S.
name: Fosalba, P.
name: Frailis, M.
name: Franceschi, E.
name: Franzetti, P.
name: Fumana, M.
name: Galeotta, S.
name: Garilli, B.
name: Gillard, W.
name: Gillis, B.
name: Giocoli, C.
name: Grazian, A.
name: Grupp, F.
name: Haugan, S. V. H.
name: Holmes, W.
name: Hormuth, F.
name: Hornstrup, A.
name: Jahnke, K.
name: Kümmel, M.
name: Kiessling, A.
name: Kilbinger, M.
name: Kitching, T.
name: Kohley, R.
name: Kurki-Suonio, H.
name: Ligori, S.
name: Lilje, P. B.
name: Lloro, I.
name: Maiorano, E.
name: Mansutti, O.
name: Marggraf, O.
name: Markovic, K.
name: Marulli, F.
name: Massey, R.
name: Maurogordato, S.
name: Meneghetti, M.
name: Merlin, E.
name: Meylan, G.
name: Moresco, M.
name: Moscardini, L.
name: Munari, E.
name: Nakajima, R.
name: Niemi, S. M.
name: Padilla, C.
name: Paltani, S.
name: Pasian, F.
name: Pedersen, K.
name: Percival, W. J.
name: Pettorino, V.
name: Pires, S.
name: Polenta, G.
name: Poncet, M.
name: Popa, L.
name: Pozzetti, L.
name: Raison, F.
name: Rebolo, R.
name: Renzi, A.
name: Rhodes, J.
name: Riccio, G.
name: Romelli, E.
name: Rosset, C.
name: Rossetti, E.
name: Saglia, R.
name: Sapone, D.
name: Sartoris, B.
name: Schneider, P.
name: Secroun, A.
name: Seidel, G.
name: Sirignano, C.
name: Sirri, G.
name: Starck, J. -L.
name: Surace, C.
name: Tallada-Crespí, P.
name: Taylor, A. N.
name: Tereno, I.
name: Toledo-Moreo, R.
name: Torradeflot, F.
name: Tutusaus, I.
name: Valentijn, E. A.
name: Valenziano, L.
name: Vassallo, T.
name: Wang, Y.
name: Weller, J.
name: Wetzstein, M.
name: Zacchei, A.
name: Zamorani, G.
name: Zoubian, J.
name: Andreon, S.
name: Maino, D.
name: Scottez, V.
name: Wright, A. H.
Publisher: name: arXiv
Year: 2022
Subjects: subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences
subject: FOS: Physical sciences

description: Cosmological constraints from key probes of the Euclid imaging survey rely critically on the accurate determination of the true redshift distributions, $n(z)$, of tomographic redshift bins. We determine whether the mean redshift, $$, of ten Euclid tomographic redshift bins can be calibrated to the Euclid target uncertainties of $σ()&lt;0.002\,(1+z)$ via cross-correlation, with spectroscopic samples akin to those from the Baryon Oscillation Spectroscopic Survey (BOSS), Dark Energy Spectroscopic Instrument (DESI), and Euclid's NISP spectroscopic survey. We construct mock Euclid and spectroscopic galaxy samples from the Flagship simulation and measure small-scale clustering redshifts up to redshift $z&lt;1.8$ with an algorithm that performs well on current galaxy survey data. The clustering measurements are then fitted to two $n(z)$ models: one is the true $n(z)$ with a free mean; the other a Gaussian Process modified to be restricted to non-negative values. We show that $$ is measured in each tomographic redshift bin to an accuracy of order 0.01 or better. By measuring the clustering redshifts on subsets of the full Flagship area, we construct scaling relations that allow us to extrapolate the method performance to larger sky areas than are currently available in the mock. For the full expected Euclid, BOSS, and DESI overlap region of approximately 6000 deg$^{2}$, the uncertainties attainable by clustering redshifts exceeds the Euclid requirement by at least a factor of three for both $n(z)$ models considered, although systematic biases limit the accuracy. Clustering redshifts are an extremely effective method for redshift calibration for Euclid if the sources of systematic biases can be determined and removed, or calibrated-out with sufficiently realistic simulations. We outline possible future work, in particular an extension to higher redshifts with quasar reference samples.
description: 14 pages, 8 figures, accepted for publication in A&amp;A