One of Euclid's key projects will be the so-called 3x2pt analysis, i.e., the combination of cosmic shear, photometric galaxy clustering, and galaxy-galaxy lensing (the cross-correlation of lens galaxy positions with the shapes of source galaxies). While Euclid has set quality requirements for the photometric redshift (photo-z) precision needed for the sources (used to measure cosmic shear),...
Understanding the nature of dark energy is one of the most important open questions in cosmology, and the large photometric survey of galaxies like Euclid will provide invaluable data on the Universe.
Accurate estimation of redshifts from photometric information is key to cosmological studies. This is often done using machine learning techniques. The selection of the spectroscopic training...
The precision of cosmological constraints derived from key observations in the Euclid imaging survey hinges on accurately measuring the first moments of the true redshift distributions of tomographic redshift bins, particularly their mean redshifts. A promising approach for achieving this is the clustering-redshifts technique, which relies on the angular cross-correlation between a target...
Neural networks have achieved remarkable success in the estimation of photometric redshifts. However, their effectiveness is significantly
compromised by the presence of sample bias in the training datasets. These networks are predominantly trained on galaxies
with spectroscopically confirmed redshifts, using these observations as proxies for the actual redshift values. This approach...
