Samuel Goldstein, Kendrick M. Smith, Utkarsh Giri, Moritz Münchmeyer

We present a method to estimate non-Gaussian power spectrum covariance matrices by directly measuring the response of the small-scale power spectrum to long-wavelength perturbations via bispectrum and trispectrum estimators. Specifically, we derive estimators for the complete non-Gaussian matter power spectrum covariance, including the super-sample contribution, in terms of the squeezed bispectrum and collapsed trispectrum of the underlying density field. We apply these estimators to the Quijote simulations, and recover unbiased estimates of the small-scale ($k\gtrsim 0.15~h/{\rm Mpc}$) matter power spectrum covariance at the percent level using only 25 simulations - comparable to the precision of the sample covariance estimated using 5,000 simulations. This technique significantly reduces the number of simulations needed to estimate power spectrum covariances and opens the possibility of inferring power spectrum covariances directly from survey data, enabling stringent tests of simulations and, potentially, power spectrum analyses that do not rely on external covariance matrices.