Hamaus, Nico; Pisani, Alice; Choi, JinAh; Lavaux, Guilhem; Wandelt, Benjamin D.; Weller, Jochen
(2020):
Precision cosmology with voids in the final BOSS data.
In: Journal of Cosmology and Astroparticle Physics, No. 12, 023

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Abstract
We report novel cosmological constraints obtained from cosmic voids in the final BOSS DR12 dataset. They arise from the joint analysis of geometric and dynamic distortions of average void shapes (i.e., the stacked voidgalaxy crosscorrelation function) in redshift space. Our model uses tomographic deprojection to infer realspace void profiles and selfconsistently accounts for the AlcockPaczynski (AP) effect and redshiftspace distortions (RSD) without any prior assumptions on cosmology or structure formation. It is derived from first physical principles and provides an extremely good description of the data at linear perturbation order. We validate this model with the help of mock catalogs and apply it to the final BOSS data to constrain the RSD and AP parameters f/b and DAH/c, where f is the linear growth rate, b the linear galaxy bias, DA the comoving angular diameter distance, H the Hubble rate, and c the speed of light. In addition, we include two nuisance parameters in our analysis to marginalize over potential systematics. We obtain f/b = 0.540 +/ 0.091 and DAH=c = 0.588 +/ 0.004 from the full void sample at a mean redshift of z = 0.51. In a at Lambda CDM cosmology, this implies Omega(m) = 0.312 +/ 0.020 for the presentday matter density parameter. When we use additional information from the survey mocks to calibrate our model, these constraints improve to f/b = 0.347 +/ 0.023, DAH/c = 0.588 +/ 0.003, and Omega(m) = 0.310 +/ 0.017. However, we emphasize that the calibration depends on the specific model of cosmology and structure formation assumed in the mocks, so the calibrated results should be considered less robust. Nevertheless, our calibrationindependent constraints are among the tightest of their kind to date, demonstrating the immense potential of using cosmic voids for cosmology in current and future data.