Deconstructing Baryon Acoustic Oscillations: A Comparison of Methods

TL;DR

This paper compares three different methods for analyzing Baryon Acoustic Oscillations to determine their effectiveness and robustness in constraining cosmological parameters, highlighting significant variations in their performance.

Contribution

It provides a comparative analysis of three BAO analysis methods, assessing their assumptions, performance, and sensitivity to systematics in future galaxy surveys.

Findings

The choice of BAO method can change the dark energy Figure of Merit by up to a factor of 35.

More information-rich methods yield higher statistical performance but are more sensitive to systematics.

The robustness of each method depends on the assumptions and the wavenumber range used in the analysis.

Abstract

The Baryon Acoustic Oscillations (BAOs) or baryon wiggles which are present in the galaxy power spectrum at scales 100-150Mpc/h are powerful features with which to constrain cosmology. The potential of these probes is such that these are now included as primary science goals in the planning of several future galaxy surveys. However, there is not a uniquely defined BAO Method in the literature but a range of implementations. We study the assumptions and cosmological performances of three different BAO methods: the full Fourier space power spectrum [P(k)], the `wiggles only' in Fourier space and the spherical harmonics power spectrum [C(l)]. We contrast the power of each method to constrain cosmology for two fiducial surveys taken from the Dark Energy Task Force (DETF) report and equivalent to future ground and space based spectroscopic surveys. We find that, depending on the assumptions used, the dark energy Figure of Merit (FoM) can change by up to a factor of 35 for a given fiducial model and survey. We compare our results with the DETF implementation and, discuss the robustness of each probe, by quantifying the dependence of the FoM with the wavenumber range. The more information used by a method, the higher its statistical performance, but the higher its sensitivity to systematics and implementations details.