Non-linear halo bias for accurate modelling of cosmic infrared background anisotropies

Abstract

Halo models provide the standard framework for interpreting cosmic infrared background (CIB) anisotropies and inferring the connection between dusty star-forming galaxies and their host dark matter halos. Recent studies have shown that inaccuracies in the modelling of halo clustering may bias the inferred parameters governing star formation in dark matter halos. In this work, we investigate whether a more detailed description of halo clustering can alleviate these shortcomings. We incorporate a scale-dependent, non-linear correction to the halo bias within a halo-model framework based on a simple SFR-halo mass parametrisation. We consider both a full mass-dependent implementation and a computationally efficient approximation. We also investigate the impact of updated (sub)halo mass functions. We validate the revised framework using mock observations derived from a dedicated simplified version of the SIDES-Uchuu simulation, combining the SIDES empirical galaxy model with the Uchuu N-body simulation, specifically designed to match the SFR prescriptions adopted in our halo-model. The corrections modify the predicted CIB clustering by up to 30% on intermediate scales, while the effective implementation reproduces the full treatment at the sub-percent level. The updated model removes the scale-dependent discrepancies previously identified and accurately reproduces the measured power spectra over the full multipole range. In MCMC analyses, the input SFR parameters are recovered with substantially reduced biases, with all input parameters recovered within their $1σ$ confidence intervals. Our results demonstrate that accurately modelling non-linear halo clustering is essential not only to reproduce CIB anisotropies, but also to reliably recover the underlying galaxy-halo connection. The framework presented here provides a validated foundation for future analyses of observational data.