Model-agnostic cosmological constraints from the baryon acoustic oscillation feature in redshift space

ABSTRACT We develop a framework for self-consistently extracting cosmological information from the clustering of tracers in redshift space, without relying on model-dependent templates to describe the baryon acoustic oscillation (BAO) feature. Our approach uses the recently proposed Laguerre reconstruction technique for the BAO feature and its linear point rLP, and substantially extends it to simultaneously model the multipoles ℓ = 0, 2, 4 of the anisotropic galaxy 2-point correlation function (2pcf). The approach is ‘model-agnostic’: it assumes that the nonlinear growth of structure smears the BAO feature by an approximately Gaussian kernel with a smearing scale σv, but does not assume any fiducial cosmology for describing the shape of the feature itself. Using mock observations for two realistic survey configurations assuming Λ cold dark matter (ΛCDM), combined with Bayesian parameter inference, we show that the linear point rLP and smearing scale σv can be accurately recovered by our method in both existing and upcoming surveys. The precision of the recovery of rLP is always better than 1{\ per\ cent}, while σv can be recovered with 10{\ per\ cent} uncertainty provided the linear galaxy bias b is separately constrained, e. g. using weak lensing observations. Our method is also sensitive to the linear growth rate f, albeit with larger uncertainties and systematic errors, especially for upcoming surveys such as DESI. We discuss how our model can be modified to improve the recovery of f, such that the resulting constraints on f, σv, rLP can potentially be used as a test of cosmological models including and beyond ΛCDM.