How to optimally combine pre-reconstruction full shape and post-reconstruction BAO signals

Abstract

We review the different approaches for combining the cosmological information from the full shape of the pre-reconstructed power spectrum - usually referred as redshift-space distortion (RSD) analysis - and from the baryon acoustic oscillation (BAO) peak position in the post-reconstructed power spectrum with the aim of finding the optimal procedure. We focus on combining the pre- and post-reconstructed derived quantities at different compression levels: 1) the two-point summary statistics, the power spectrum multipoles, $P^{(\ell )}(k)$; 2) the compressed BAO variables, ${\alpha }_{\parallel ,\perp }$; and 3) an hybrid approach between 1) and 2). We apply these methods to the publicly available eBOSS Luminous Red Galaxy catalogues, for both data and synthetic EZ-mocks. We find that the three approaches result in very consistent posteriors when the appropriate covariance matrix estimator is used. On average, the combination at $P^{(\ell )}(k)$ level retrieves $5-10\%$ tighter constraints than the other two approaches, demonstrating that the standard approach of combining at the level of the BAO variables is nearly optimal. We conclude that combining both BAO post-reconstructed and full shape pre-reconstructed signals for the one single data realization at the level of the summary statistics is faster, as it does not require running the whole pipeline on the individual mocks, and brings a moderate $10\%$ improvement, with respect to the other two studied methods. Moreover, we check for potential systematics, such as, the way the matrix is built and the effect of the finite number of mocks on the likelihood estimator and find none of these have a significant impact in the final results. Combining the pre- and post-reconstruction signals at the level of the summary statistics is an attractive, faster and accurate method to be used in future and on-going spectroscopic surveys.

arXiv comment29 pages, 13 figures, 5 tables. Version accepted for publication in JCAP

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