Author:
,Lepori F.,Tutusaus I.,Viglione C.,Bonvin C.,Camera S.,Castander F. J.,Durrer R.,Fosalba P.,Jelic-Cizmek G.,Kunz M.,Adamek J.,Casas S.,Martinelli M.,Sakr Z.,Sapone D.,Amara A.,Auricchio N.,Bodendorf C.,Bonino D.,Branchini E.,Brescia M.,Brinchmann J.,Capobianco V.,Carbone C.,Carretero J.,Castellano M.,Cavuoti S.,Cimatti A.,Cledassou R.,Congedo G.,Conselice C. J.,Conversi L.,Copin Y.,Corcione L.,Courbin F.,Da Silva A.,Degaudenzi H.,Douspis M.,Dubath F.,Dupac X.,Dusini S.,Ealet A.,Farrens S.,Ferriol S.,Franceschi E.,Fumana M.,Garilli B.,Gillard W.,Gillis B.,Giocoli C.,Grazian A.,Grupp F.,Guzzo L.,Haugan S. V. H.,Holmes W.,Hormuth F.,Hudelot P.,Jahnke K.,Kermiche S.,Kiessling A.,Kilbinger M.,Kitching T.,Kümmel M.,Kurki-Suonio H.,Ligori S.,Lilje P. B.,Lloro I.,Mansutti O.,Marggraf O.,Markovic K.,Marulli F.,Massey R.,Maurogordato S.,Melchior M.,Meneghetti M.,Merlin E.,Meylan G.,Moresco M.,Moscardini L.,Munari E.,Nakajima R.,Niemi S. M.,Padilla C.,Paltani S.,Pasian F.,Pedersen K.,Percival W. J.,Pettorino V.,Pires S.,Poncet M.,Popa L.,Pozzetti L.,Raison F.,Rhodes J.,Roncarelli M.,Rossetti E.,Saglia R.,Schneider P.,Secroun A.,Seidel G.,Serrano S.,Sirignano C.,Sirri G.,Stanco L.,Starck J.-L.,Tallada-Crespí P.,Taylor A. N.,Tereno I.,Toledo-Moreo R.,Torradeflot F.,Valentijn E. A.,Valenziano L.,Wang Y.,Weller J.,Zamorani G.,Zoubian J.,Andreon S.,Bardelli S.,Fabbian G.,Graciá-Carpio J.,Maino D.,Medinaceli E.,Mei S.,Renzi A.,Romelli E.,Sureau F.,Vassallo T.,Zacchei A.,Zucca E.,Baccigalupi C.,Balaguera-Antolínez A.,Bernardeau F.,Biviano A.,Blanchard A.,Bolzonella M.,Borgani S.,Bozzo E.,Burigana C.,Cabanac R.,Cappi A.,Carvalho C. S.,Castignani G.,Colodro-Conde C.,Coupon J.,Courtois H. M.,Cuby J.-G.,Davini S.,de la Torre S.,Di Ferdinando D.,Farina M.,Ferreira P. G.,Finelli F.,Galeotta S.,Ganga K.,Garcia-Bellido J.,Gaztanaga E.,Gozaliasl G.,Hook I. M.,Ilić S.,Joachimi B.,Kansal V.,Keihanen E.,Kirkpatrick C. C.,Lindholm V.,Mainetti G.,Maoli R.,Martinet N.,Maturi M.,Metcalf R. B.,Monaco P.,Morgante G.,Nightingale J.,Nucita A.,Patrizii L.,Popa V.,Potter D.,Riccio G.,Sánchez A. G.,Schirmer M.,Schultheis M.,Scottez V.,Sefusatti E.,Tramacere A.,Valiviita J.,Viel M.,Hildebrandt H.
Abstract
Aims. We investigate the importance of lensing magnification for estimates of galaxy clustering and its cross-correlation with shear for the photometric sample of Euclid. Using updated specifications, we study the impact of lensing magnification on the constraints and the shift in the estimation of the best fitting cosmological parameters that we expect if this effect is neglected.
Methods. We follow the prescriptions of the official Euclid Fisher matrix forecast for the photometric galaxy clustering analysis and the combination of photometric clustering and cosmic shear. The slope of the luminosity function (local count slope), which regulates the amplitude of the lensing magnification, and the galaxy bias have been estimated from the Euclid Flagship simulation.
Results. We find that magnification significantly affects both the best-fit estimation of cosmological parameters and the constraints in the galaxy clustering analysis of the photometric sample. In particular, including magnification in the analysis reduces the 1σ errors on Ωm, 0, w0, wa at the level of 20–35%, depending on how well we will be able to independently measure the local count slope. In addition, we find that neglecting magnification in the clustering analysis leads to shifts of up to 1.6σ in the best-fit parameters. In the joint analysis of galaxy clustering, cosmic shear, and galaxy–galaxy lensing, magnification does not improve precision, but it leads to an up to 6σ bias if neglected. Therefore, for all models considered in this work, magnification has to be included in the analysis of galaxy clustering and its cross-correlation with the shear signal (3 × 2pt analysis) for an accurate parameter estimation.