Redshift Weights for Baryon Acoustic Oscillations : Application to Mock Galaxy Catalogs

Abstract

Large redshift surveys capable of measuring the Baryon Acoustic Oscillation (BAO) signal have proven to be an effective way of measuring the distance-redshift relation in cosmology. Building off the work in Zhu et al. (2015), we develop a technique to directly constrain the distance-redshift relation from BAO measurements without splitting the sample into redshift bins. We parametrize the distance-redshift relation, relative to a fiducial model, as a quadratic expansion. We measure its coefficients and reconstruct the distance-redshift relation from the expansion. We apply the redshift weighting technique in Zhu et al. (2015) to the clustering of galaxies from 1000 QuickPM (QPM) mock simulations after reconstruction and achieve a 0.75% measurement of the angular diameter distance $D_A$ at $z=0.64$ and the same precision for Hubble parameter H at $z=0.29$. These QPM mock catalogs are designed to mimic the clustering and noise level of the Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 12 (DR12). We compress the correlation functions in the redshift direction onto a set of weighted correlation functions. These estimators give unbiased $D_A$ and $H$ measurements at all redshifts within the range of the combined sample. We demonstrate the effectiveness of redshift weighting in improving the distance and Hubble parameter estimates. Instead of measuring at a single 'effective' redshift as in traditional analyses, we report our $D_A$ and $H$ measurements at all redshifts. The measured fractional error of $D_A$ ranges from 1.53% at $z=0.2$ to 0.75% at $z=0.64$. The fractional error of $H$ ranges from 0.75% at $z=0.29$ to 2.45% at $z=0.7$. Our measurements are consistent with a Fisher forecast to within 10% to 20% depending on the pivot redshift. We further show the results are robust against the choice of fiducial cosmologies, galaxy bias models, and RSD streaming parameters.

13 pages, 8 figures, submitted to MNRAS

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