Mitigating prior-volume effects in galaxy clustering by adopting Mpc over Mpc/h units

Abstract

We investigate prior-volume effects in Bayesian analyses of galaxy clustering based on state-of-the-art models, such as the EFT and the VDG$_\infty$ model. We show that these effects are largely driven by imposing priors on the model parameters defined in ${\rm Mpc}/h$ units, thereby introducing a spurious correlation with the Hubble parameter $h$, and that they can be strongly mitigated by adopting a more physically motivated formulation in ${\rm Mpc}$ units. We test this approach using a set of synthetic data vectors designed to reproduce the clustering signal expected in a Stage-IV survey across a range of cosmological models, including $Λ$CDM, $K$CDM, $w$CDM, and $w_0w_a$CDM. We find that the resulting constraints are largely free from shifts induced during the marginalisation process, even in the challenging $w_0w_a$CDM runs, reducing discrepancies with the fiducial values from several standard deviations to well within $1σ$. The performance of this approach is comparable to that of alternative mitigation strategies, such as the adoption of invariant priors or the reparametrisation of nuisance parameters in terms of growth and geometrical amplitudes. For the latter, we find that using $σ_{12}$ in place of the commonly adopted $σ_8$ to describe the amplitude of density fluctuations improves the performance of the reparametrisation technique, even at high redshifts, where constraints on the dark-energy equation of state are weaker. We further show that these conclusions hold with the inclusion of additional BAO constraints, and for data sets with reduced statistical power. These results add to the benefits of working with ${\rm Mpc}$ units, avoiding an explicit dependence on $h$, with direct relevance for the analyses of Stage-IV galaxy surveys.