Euclid preparation

Paykari, P.; Kitching, T.; Hoekstra, H.; Azzollini, R.; Cardone, V. F.; Cropper, M.; Duncan, C. A. J.; Kannawadi, A.; Miller, L.; Aussel, H.; Conti, I. F.; Auricchio, N.; Baldi, M.; Bardelli, S.; Biviano, A.; Bonino, D.; Borsato, E.; Bozzo, E.; Branchini, E.; Brau-Nogue, S.; Brescia, M.; Brinchmann, J.; Burigana, C.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Castander, F. J.; Castellano, M.; Cavuoti, S.; Charles, Y.; Cledassou, R.; Colodro-Conde, C.; Congedo, G.; Conselice, C.; Conversi, L.; Copin, Y.; Coupon, J.; Courtois, H. M.; da Silva, A.; Dupac, X.; Fabbian, G.; Farrens, S.; Ferreira, P. G.; Fosalba, P.; Fourmanoit, N.; Frailis, M.; Fumana, M.; Galeotta, S.; Garilli, B.; Gillard, W.; Gillis, B. R.; Giocoli, C.; Gracia-Carpio, J.; Grupp, F.; Hormuth, F.; Ilic, S.; Israel, H.; Jahnke, K.; Keihanen, E.; Kermiche, S.; Kilbinger, M.; Kirkpatrick, C. C.; Kubik, B.; Kunz, M.; Kurki-Suonio, H.; Laureijs, R.; Le Mignant, D.; Ligori, S.; Lilje, P. B.; Lloro, I.; Maciaszek, T.; Maiorano, E.; Marggraf, O.; Markovic, K.; Martinet, N.; Marulli, F.; Massey, R.; Mauri, N.; Medinaceli, E.; Mei, S.; Mellier, Y.; Meneghetti, M.; Metcalf, R. B.; Moresco, M.; Moscardini, L.; Munari, E.; Neissner, C.; Nichol, R. C.; Niemi, S.; Nutma, T.; Padilla, C.; Paltani, S.; Pasian, F.; Pettorino, V.; Pires, S.; Polenta, G.; Raison, F.; Renzi, A.; Rhodes, J.; Romelli, E.; Roncarelli, M.; Rossetti, E.; Saglia, R.; Sakr, Z.; Sanchez, A. G.; Sapone, D.; Scaramella, R.; Schneider, P.; Schrabback, T.; Scottez, V.; Secroun, A.; Serrano, S.; Sirignano, C.; Sirri, G.; Stanco, L.; Starck, J. -L.; Sureau, F.; Tallada-Crespi, P.; Taylor, A.; Tenti, M.; Tereno, I.; Toledo-Moreo, R.; Torradeflot, F.; Valenziano, L.; Vannier, M.; Vassallo, T.; Zoubian, J.; Zucca, E.

doi:10.1051/0004-6361/201936980

Aims. Our aim is to quantify the impact of systematic effects on the inference of cosmological parameters from cosmic shear. Methods. We present an "end-to-end" approach that introduces sources of bias in a modelled weak lensing survey on a galaxy-by-galaxy level. We propagated residual biases through a pipeline from galaxy properties at one end to cosmic shear power spectra and cosmological parameter estimates at the other end. We did this to quantify how imperfect knowledge of the pipeline changes the maximum likelihood values of dark energy parameters. Results. We quantify the impact of an imperfect correction for charge transfer inefficiency and modelling uncertainties of the point spread function for Euclid, and find that the biases introduced can be corrected to acceptable levels.