The correlation matrix of the jackknife covariance (top left) is compared to the same matrix after jackknife debiasing (bottom right). See Fig. 5 for details on the subplot layout. Debiasing produces a noisy covariance, especially in regions of low ℓ where the assumptions of the jackknife (i.e. that the samples can be treated as independent) break down. In contrast, linear shrinkage provides a covariance with lower noise-properties more closely matching the structure of the sample covariance correlation matrix.