Aims: The precision of cosmological constraints from imaging surveys hinges on an accurately estimated redshift distribution n (z) of the tomographic bins, especially their mean redshifts. We assess the effectiveness of the clustering-redshifts technique in constraining Euclid tomographic redshift bins to meet the target uncertainty of σ (łangle z ̊angle) < 0. 002 (1 + z). We inferred these mean redshifts from the small-scale angular clustering of Euclid galaxies, which were distributed into bins with spectroscopic samples localised in narrow redshift slices. Methods: We generated spectroscopic mocks from the Flagship2 simulation for the Baryon Oscillation Spectroscopic Survey (BOSS), the Dark Energy Spectroscopic Instrument (DESI), and the Euclid Near-Infrared Spectrometer and Photometer (NISP) spectroscopic survey. We evaluated and optimised the clustering-redshifts pipeline, and we introduced a new method for measuring the photometric galaxy bias (clustering), which is the primary limitation of this technique. Results: We have successfully constrained the means and standard deviations of the redshift distributions for all of the tomographic bins (with a maximum photometric redshift of 1. 6). We achieved precision beyond the required thresholds. We have identified the main sources of bias, particularly the impact of the one-halo galaxy distribution, which imposed the minimal separation scale to be larger than 1. 5 Mpc for evaluating cross-correlations. These results demonstrate that clustering-redshifts can meet the precision requirements for Euclid, and we highlighted several avenues for future improvements.
D. et al. (Thu,) studied this question.