An Inventory of Bispectrum Estimators for Redshift Space Distortions

TL;DR

This paper reviews various bispectrum estimators for redshift space distortions, including a new modal decomposition method, to improve data analysis efficiency and robustness in galaxy surveys.

Contribution

It introduces a suite of alternative bispectrum estimators, notably a modal decomposition, for efficient and robust measurement of three-point correlations in galaxy surveys.

Findings

Modal estimator offers significant data compression.

Alternative estimators provide robustness checks.

Potential reduction in computational load.

Abstract

In order to best improve constraints on cosmological parameters and on models of modified gravity using current and future galaxy surveys it is necessary maximally exploit the available data. As redshift-space distortions mean statistical translation invariance is broken for galaxy observations, this will require measurement of the monopole, quadrupole and hexadecapole of not just the galaxy power spectrum, but also the galaxy bispectrum. A recent (2015) paper by Scoccimarro demonstrated how the standard bispectrum estimator may be expressed in terms of Fast Fourier Transforms (FFTs) to afford an extremely efficient algorithm, allowing the bispectrum multipoles on all scales and triangle shapes to be measured in comparable time to those of the power spectrum. In this paper we present a suite of alternative proxies to measure the three-point correlation multipoles. In particular, we describe a modal (or plane wave) decomposition to capture the information in each multipole in a series of basis coefficients, and also describe three compressed estimators formed using the skew-spectrum, the line correlation function and the integrated bispectrum, respectively. As well as each of the estimators offering a different measurement channel, and thereby a robustness check, it is expected that some (especially the modal estimator) will offer a vast data compression, and so a much reduced covariance matrix. This compression may be vital to reduce the computational load involved in extracting the available three-point information.