Cosmological Parameter Forecasts By A Joint 2d Tomographic Approach To Cmb And Galaxy Clustering

The cross-correlation between the cosmic microwave background (CMB) fields and matter tracers carries important cosmological information. In this paper, we forecast by a signal-to-noise ratio analysis the information contained in the cross-correlation of the CMB anisotropy fields with source counts for future cosmological observations and its impact on cosmological parameters uncertainties, using a joint tomographic analysis. We include temperature, polarization, and lensing for the CMB fields and galaxy number counts for the matter tracers. We consider Planck-like, the Simons Observatory, LiteBIRD, and CMB-S4 specifications for CMB, and Euclid-like, Vera C. Rubin Observatory, SPHEREx, EMU, and SKA1 for future galaxy surveys. We restrict ourselves to quasilinear scales in order to deliver results that are free as much as possible from the uncertainties in modeling nonlinearities. We forecast by a Fisher matrix formalism the relative importance of the cross-correlation of source counts with the CMB in the constraints on the parameters for several cosmological models. We obtain that the CMB-number counts cross-correlation can improve the dark energy figure of merit (FOM) at most up to a factor similar to 2 for LiteBIRD + CMB-S4 x SKA1 compared to the uncorrelated combination of both probes and will enable the Euclid-like photometric survey to reach the highest FOM among those considered here. We also forecast how CMB-galaxy clustering cross-correlation could increase the FOM of the neutrino sector, also enabling a statistically significant (greater than or similar to 3 sigma for LiteBIRD + CMB-S4 x SPHEREx) detection of the minimal neutrino mass allowed in a normal hierarchy by using quasilinear scales only. Analogously, we find that the uncertainty in the local primordial non-Gaussianity could be as low as sigma(f(NL)) similar to 1.5-2 by using two-point statistics only with the combination of CMB and radio surveys, such as EMU and SKA1. Further, we quantify how cross-correlation will help characterizing the galaxy bias. Our results highlight the additional constraining power of the cross-correlation between CMB and galaxy clustering from future surveys, which is mainly based on quasilinear scales and therefore, sufficiently robust to nonlinear effects.