<i>Euclid</i>preparation

The accuracy of photometric redshifts (photo-zs) particularly affects the results analyses galaxy clustering with photometrically-selected galaxies (GCph) and weak lensing. In next decade, space missions like Euclid will collect measurements for millions galaxies. These data should be complemented upcoming ground-based observations to derive precise accurate photo-zs. this paper, we explore how tomographic redshift binning depth affect cosmological constraints expected from Euclid. We focus on GCph extend study include galaxy-galaxy lensing (GGL). add a layer complexity analysis by simulating several realistic photo-z distributions based Consortium Flagship simulation using machine learning algorithm. use Fisher matrix formalism these samples constraining power as function binning, survey depth, accuracy. find that bins equal width in provide higher Figure Merit (FoM) than equipopulated increasing number 10 13 improves FoM 35% 15% its combination GGL, respectively. For GCph, an increase provides FoM. But addition faint beyond limit spectroscopic training decreases due spurious When combining both probes, density sample, which is set main factor driving variations conclude there more information can extracted nominal cautious when adding into our since they degrade constraints.