On the linearity of tracer bias around voids

TL;DR

This paper demonstrates that tracer bias around cosmic voids can be effectively described by a linear relation, simplifying the modeling of large-scale structure and aiding cosmological parameter estimation.

Contribution

It shows that void-tracer cross-correlations follow a linear bias relation, linking tracer density profiles to matter density, which simplifies analysis of cosmic structures.

Findings

Void-tracer cross-correlations are well-described by linear bias.

The bias matches large-scale auto-correlation estimates for large voids.

Small voids show a higher bias, affecting cosmological inferences.

Abstract

The large-scale structure of the universe can only be observed via luminous tracers of the dark matter. However, the clustering statistics of tracers are biased and depend on various properties, such as their host-halo mass and assembly history. On very large scales this tracer bias results in a constant offset in the clustering amplitude, known as linear bias. Towards smaller nonlinear scales, this is no longer the case and tracer bias becomes a complicated function of scale and time. We focus on tracer bias centred on cosmic voids, depressions of the density field that spatially dominate the universe. We consider three types of tracers: galaxies, galaxy clusters and AGN, extracted from the hydrodynamical simulation Magneticum Pathfinder. In contrast to common clustering statistics that focus on auto-correlations of tracers, we find that void-tracer cross-correlations are successfully described by a linear-bias relation. The tracer-density profile of voids can thus be related to their matter-density profile by a single number. We show that it coincides with the linear tracer bias extracted from the large-scale auto-correlation function and expectations from theory, if sufficiently large voids are considered. For smaller voids we observe a shift towards higher values. This has important consequences on cosmological parameter inference, as the problem of unknown tracer bias is alleviated up to a constant number. The smallest scales in existing datasets become accessible to simpler models, providing numerous modes of the density field that have been disregarded so far, but may help to further reduce statistical errors in constraining cosmology.