Euclid preparation

We model intrinsic alignment (IA) in Euclid ’s Flagship simulation to investigate its impact on Euclid ’s weak lensing signal. Our IA implementation in the Flagship simulation takes the photometric properties of galaxies into account, along with their dark matter host halos. The simulation parameters are calibrated using combined constraints from observations and cosmological hydrodynamical simulations. We compare simulations against theory predictions, determining the parameters of two of the most widely used IA models: the nonlinear alignment (NLA) and the tidal alignment and tidal torquing (TATT) models. We measured the amplitude of the simulated IA signal as a function of galaxy magnitude and colour in the redshift range 0.1 < z < 2.1, close that of Euclid ’s main galaxy sample. We find that both NLA and TATT are able to accurately describe the IA signal in the simulation down to scales of 6–7 h −1 Mpc. We measured the alignment amplitudes for red galaxies that are comparable to those of the observations, using samples that had not been used in the initial calibration procedure. For blue galaxies, our constraints are consistent with zero alignments in our first redshift bin 0.1 < z < 0.3, but we detected a non-negligible signal at higher redshift; however, this signal is consistent with the upper limits set by observational constraints. Additionally, several hydrodynamical simulations have predicted alignments for spiral galaxies, in agreement with our findings. Finally, the evolution of alignment with redshift is realistic and comparable to what has been determined based on observations. However, we find that the commonly adopted redshift power law for IA fails to reproduce the simulation alignments above z = 1.1. A significantly improved agreement can be obtained when the luminosity dependence is included, capturing the intrinsic luminosity evolution with redshift in magnitude-limited surveys. We conclude that the Flagship IA simulation is a useful tool for translating current IA constraints into predictions for IA contamination of Euclid -like samples.

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