Shrinkage Estimation of the Power Spectrum Covariance Matrix

TL;DR

This paper introduces a shrinkage estimation technique to improve the accuracy of power spectrum covariance matrices from limited simulations, significantly enhancing cosmological parameter estimation.

Contribution

The paper presents a novel shrinkage estimation method that combines empirical covariance with a model to reduce noise and improve covariance estimates in cosmology.

Findings

Shrinkage estimator outperforms empirical covariance with few simulations.

Reducing noise improves cosmological parameter estimates.

Method extends effectively to jackknife covariance estimation.

Abstract

We seek to improve estimates of the power spectrum covariance matrix from a limited number of simulations by employing a novel statistical technique known as shrinkage estimation. The shrinkage technique optimally combines an empirical estimate of the covariance with a model (the target) to minimize the total mean squared error compared to the true underlying covariance. We test this technique on N-body simulations and evaluate its performance by estimating cosmological parameters. Using a simple diagonal target, we show that the shrinkage estimator significantly outperforms both the empirical covariance and the target individually when using a small number of simulations. We find that reducing noise in the covariance estimate is essential for properly estimating the values of cosmological parameters as well as their confidence intervals. We extend our method to the jackknife covariance estimator and again find significant improvement, though simulations give better results. Even for thousands of simulations we still find evidence that our method improves estimation of the covariance matrix. Because our method is simple, requires negligible additional numerical effort, and produces superior results, we always advocate shrinkage estimation for the covariance of the power spectrum and other large-scale structure measurements when purely theoretical modeling of the covariance is insufficient.