Extraction of the Baryon Acoustic Oscillations (BAO) to percent level accuracy is challenging and demands an understanding of many potential systematic to an accuracy well below 1 per cent, in order ensure that they do not combine significantly when
compared to statistical error of the BAO measurement. Sloan Digital Sky Survey (SDSS)-III Baryon Oscillation Spectroscopic Survey (BOSS) SDSS Data Release Eleven (DR11) reaches a distance measurement with $sim 1%$ statistical error and this prompts an extensive search for all possible sub-percent level systematic errors which could be safely ignored previously. In this paper, we analyze the potential systematics in BAO fitting methodology using mocks and data from BOSS DR10 and DR11. We demonstrate the robustness of the fiducial multipole fitting methodology to be at $0.1%-0.2%$ level with a wide range of tests in mock galaxy catalogs pre- and post-reconstruction. We also find the DR10 and DR11 data from BOSS to be robust against changes in methodology at similar level. This systematic error budget is incorporated into the the error budget of Baryon Oscillation Spectroscopic Survey (BOSS) DR10 and DR11 BAO measurements. Of the wide range of changes we have investigated, we find that when fitting pre-reconstructed data or mocks, the following changes have the largest effect on the best fit values of distance measurements both parallel and perpendicular to the line of sight: (a) Changes in non-linear correlation function template; (b) Changes in fitting range of the correlation function; (c) Changes to the non-linear damping model parameters. The priors applied do not matter in the estimates of the fitted errors as long as we restrict ourselves to physically meaningful fitting regions.[abridged]
Measuring the two-point correlation function of the galaxies in the Universe gives access to the underlying dark matter distribution, which is related to cosmological parameters and to the physics of the primordial Universe. The estimation of the cor
relation function for current galaxy surveys makes use of the Landy-Szalay estimator, which is supposed to reach minimal variance. This is only true, however, for a vanishing correlation function. We study the Landy-Szalay estimator when these conditions are not fulfilled and propose a new estimator that provides the smallest variance for a given survey geometry. Our estimator is a linear combination of ratios between paircounts of data and/or random catalogues (DD, RR and DR). The optimal combination for a given geometry is determined by using lognormal mock catalogues. The resulting estimator is biased in a model-dependent way, but we propose a simple iterative procedure for obtaining an unbiased model- independent estimator.Our method can be easily applied to any dataset and requires few extra mock catalogues compared to the standard Landy-Szalay analysis. Using various sets of simulated data (lognormal, second-order LPT and N-Body), we obtain a 20-25% gain on the error bars on the two-point correlation function for the SDSS geometry and $Lambda$CDM correlation function. When applied to SDSS data (DR7 and DR9), we achieve a similar gain on the correlation functions, which translates into a 10-15% improvement over the estimation of the densities of matter $Omega_m$ and dark energy $Omega_Lambda$ in an open $Lambda$CDM model. The constraints derived from DR7 data with our estimator are similar to those obtained with the DR9 data and the Landy-Szalay estimator, which covers a volume twice as large and has a density that is three times higher.
