How accurately can we measure the baryon acoustic oscillation feature?

• Published in: arXiv.org
• Main Authors: Ruggeri, Rossana, Blake, Chris
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 28.09.2019
• Subjects: Acoustic noise; Astronomical models; Baryons; Conditional probability; Confidence; Cosmology; Dark energy; Error analysis; Galaxy distribution; Maximum likelihood estimators; Noise; Physics - Cosmology and Nongalactic Astrophysics; Sample variance; Variance
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1909.13011

Baryon acoustic oscillations (BAO) represent one of the cleanest probes of dark energy, allowing for tests of the cosmological model through the measurement of distance and expansion rate from a 3D galaxy distribution. The signal appears at large scales in the correlation function where linear theory applies, allowing for the construction of accurate models. However, due to the lower number of modes available at these scales, sample variance has a significant impact on the signal, and may sharpen or widen the underlying peak. Therefore, equivalent mock realizations of a galaxy survey present different errors in the position of the peak when uncertainties are estimated from the posterior probability distribution corresponding to the individual mocks. Hence the posterior width, often quoted as the error in BAO survey measurements, is subject to sample noise. A different definition of the error is provided by the asymptotic variance of the maximum likelihood estimator, which involves the average over multiple realizations, and is not subject to sample noise. In this work we re-analyse the main galaxy survey data available for BAO measurements and quantify the impact of the noise component on the error quoted for BAO measurements. We quantify the difference between three definitions of the error: the confidence region computed from a single posterior, the average of the variances of many realizations, and the Fisher matrix prediction assuming a Gaussian likelihood.