  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.
