Non-Gaussian error bars in galaxy surveys -- II

TL;DR

This paper introduces a novel method to directly measure non-Gaussian error bars on the matter power spectrum from galaxy survey data, overcoming limitations of Gaussian assumptions and simulation-based approaches.

Contribution

It develops a new technique using symmetries, Wiener filtering, and PCA to estimate the full covariance matrix from only four independent galaxy survey fields.

Findings

Recovered Fisher information within 20% of large sample results for k < 1.0 h/Mpc

Provided a practical prescription for extracting non-Gaussian covariance matrices from limited data

Demonstrated the method's effectiveness with minimal prior assumptions

Abstract

(Abridged) Estimating the uncertainty on the matter power spectrum internally (i.e. directly from the data) is made challenging by the simple fact that galaxy surveys offer at most a few independent samples. In addition, surveys have non-trivial geometries, which make the interpretation of the observations even trickier, but the uncertainty can nevertheless be worked out within the Gaussian approximation. With the recent realization that Gaussian treatments of the power spectrum lead to biased error bars about the dilation of the baryonic acoustic oscillation scale, efforts are being directed towards developing non-Gaussian analyses, mainly from N-body simulations so far. Unfortunately, there is currently no way to tell how the non-Gaussian features observed in the simulations compare to those of the real Universe, and it is generally hard to tell at what level of accuracy the N-body simulations can model complicated non-linear effects such as mode coupling and galaxy bias. We propose in this paper a novel method that aims at measuring non-Gaussian error bars on the matter power spectrum directly from galaxy survey data. We utilize known symmetries of the 4-point function, Wiener filtering and principal component analysis to estimate the full covariance matrix from only four independent fields with minimal prior assumptions. With the noise filtering techniques and only four fields, we are able to recover the Fisher information obtained from a large N=200 sample to within 20 per cent, for k < 1.0 h/Mpc. Finally, we provide a prescription to extract a noise-filtered, non-Gaussian, covariance matrix from a handful of fields in the presence of a survey selection function.