Euclid: Inferring star-formation history via cross-correlations of photometric clustering and shear with the cosmic infrared background
The cosmic infrared background, sourced by dust heated by star-forming activity, encodes the integrated history of the star-formation distribution over a large swathe of cosmic time. Decomposing the contribution from galaxies at different redshifts to the observed CIB maps provides a powerful tool for probing the large-scale star-formation history across cosmic times. To this end, we perform a forecast tomographic cross-correlation analysis using simulated photometric galaxy clustering and weak lensing data based on the Euclid mission survey specifications, combined with Planck CIB observations. The analysis adopts templates constructed from the halo occupation distribution model to measure the bias-weighted star-formation-rate density, $\langle b \rho_{\rm SFRD}\rangle$, as a function of redshift. We present forecasts for the $\langle b \rho_{\rm SFRD}\rangle$ constraints based on these simulations and quantify the expected constraining power using Fisher information matrix methods. Compared with results obtained using the same dataset as in previous works, we find that Euclid will tighten the constraints on $\langle b \rho_{\rm SFRD}\rangle$ across all redshift bins by a factor of about $2$ and provide more measurements over a deeper redshift range. This leads to improved constraints on all HOD parameters, typically reducing the marginalized uncertainties by a factor of about $2.5$, while also breaking parameter degeneracies.