Non-fiducial cosmological test from geometrical and dynamical distortions around voids

• Published in: Monthly notices of the Royal Astronomical Society Vol. 485; no. 4; pp. 5761 - 5772
• Main Authors: Correa, Carlos M, Paz, Dante J, Padilla, Nelson D, Ruiz, Andrés N, Angulo, Raúl E, Sánchez, Ariel G
• Format: Journal Article
• Language: English
• Published: Oxford University Press 01-06-2019
• Subjects: methods: statistical; methods: data analysis; galaxies: distances and redshifts; cosmological parameters; large-scale structure of Universe
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1093/mnras/stz821

Abstract
We present a new cosmological test using the distribution of galaxies around cosmic voids without assuming a fiducial cosmology. The test is based on a physical model for the void–galaxy cross-correlation function projected along and perpendicular to the line of sight. We treat correlations in terms of void-centric angular distances and redshift differences between void–galaxy pairs, hence it is not necessary to assume a fiducial cosmology. This model reproduces the coupled dynamical (Kaiser effect, RSD) and geometrical (Alcock–Paczynski effect, GD) distortions that affect the correlation measurements. It also takes into account the scale mixing due to the projection ranges in both directions. The model is general, so it can be applied to an arbitrary cylindrical binning scheme, not only in the case of the projected correlations. It primarily depends on two cosmological parameters: Ωm, the matter fraction of the Universe today (sensitive to GD), and β, the ratio between the growth rate factor of density perturbations and the tracer bias (sensitive to RSD). In the context of the new generation of galaxy spectroscopic surveys, we calibrated the test using the Millennium XXL simulation for different redshifts. The method successfully recovers the cosmological parameters. We studied the effect of measuring with different projection ranges, finding robust results up to wide ranges. The resulting data covariance matrices are relatively small, which reduces the noise in the Gaussian likelihood analysis and will allow the usage of a smaller number of mock catalogues. The performance evaluated in this work indicates that the developed method is a promising test to be applied on real data.