Euclid: Forecasts from the void-lensing cross-correlation

The Euclid space telescope will survey a large dataset of cosmic voids traced by dense samples of galaxies. In this work we estimate its expected performance when exploiting angular photometric void clustering, galaxy weak lensing, and their cross-correlation. To this aim, we implemented a Fisher matrix approach tailored for voids from the Euclid photometric dataset and we present the first forecasts on cosmological parameters that include the void-lensing correlation. We examined two different probe settings, pessimistic and optimistic, both for void clustering and galaxy lensing. We carried out forecast analyses in four model cosmologies, accounting for a varying total neutrino mass, M-nu, and a dynamical dark energy (DE) equation of state, w(z), described by the popular Chevallier-Polarski-Linder parametrization. We find that void clustering constraints on h and Omega(b) are competitive with galaxy lensing alone, while errors on n(s) decrease thanks to the orthogonality of the two probes in the 2D-projected parameter space. We also note that, as a whole, with respect to assuming the two probes as independent, the inclusion of the void-lensing cross-correlation signal improves parameter constraints by 10-15%, and enhances the joint void clustering and galaxy lensing figure of merit (FoM) by 10% and 25%, in the pessimistic and optimistic scenarios, respectively. Finally, when further combining with the spectroscopic galaxy clustering, assumed as an independent probe, we find that, in the most competitive case, the FoM increases by a factor of 4 with respect to the combination of weak lensing and spectroscopic galaxy clustering taken as independent probes. The forecasts presented in this work show that photometric void clustering and its cross-correlation with galaxy lensing deserve to be exploited in the data analysis of the Euclid galaxy survey and promise to improve its constraining power, especially on h, Omega(b), the neutrino mass, and the DE evolution.