Orateur
Description
Euclid is an ongoing space-based cosmological survey designed to map the large-scale structure of the Universe by observing billions of galaxies. Its primary goal is to investigate the nature of dark matter and dark energy and to place tight constraints on the cosmological model.
A key probe for Euclid is galaxy weak gravitational lensing, which measures the small distortions induced in the observed shapes of background galaxies by the gravitational fields of foreground matter. Combined with galaxy clustering, weak lensing provides a powerful means of probing the distribution and evolution of matter throughout the Universe.
A crucial ingredient for extracting robust cosmological constraints from these measurements is an accurate determination of the redshift distribution of the galaxies entering the analysis. Since Euclid primarily relies on photometric observations, galaxy redshifts are estimated from their observed colours and fluxes. Calibrating the relationship between galaxy colours and redshift is therefore an essential step in the analysis and represents one of the main sources of systematic uncertainty in the cosmological parameter constraints.
It is thus essential to properly characterize, control, and mitigate the various sources of uncertainty affecting the inferred redshift distributions.
In this talk, we use a synthetic source-injection pipeline to characterize the Euclid galaxy selection function and photometric noise properties in both the Wide and Deep surveys. The Deep Survey benefits from substantially deeper imaging, enabling more accurate redshift estimates and providing a reference sample for calibrating the redshift distributions of galaxies in the Wide Survey.
We then apply the Euclid selection function to the state-of-the-art N-body Flagship 2 (FS2) simulation to generate realistic Euclid-like galaxy mock catalogues. These mocks are used to perform the redshift calibration and quantify the different sources of systematic uncertainty. Finally, we investigate how these uncertainties can be consistently propagated into the cosmological analysis, providing informed priors for the likelihood and assessing their impact on the resulting cosmological parameter constraints.