Analytic Gaussian Covariance Matrices for Galaxy $N$-Point Correlation
  Functions
  We derive analytic covariance matrices for the $N$-Point Correlation
Functions (NPCFs) of galaxies in the Gaussian limit. Our results are given for
arbitrary $N$ and projected onto the isotropic basis functions of Cahn &
Slepian (2020), recently shown to facilitate efficient NPCF estimation. A
numerical implementation of the 4PCF covariance is compared to the sample
covariance obtained from a set of lognormal simulations, Quijote dark matter
halo catalogues, and MultiDark-Patchy galaxy mocks, with the latter including
realistic survey geometry. The analytic formalism gives reasonable predictions
for the covariances estimated from mock simulations with a periodic-box
geometry. Furthermore, fitting for an effective volume and number density by
maximizing a likelihood based on Kullback-Leibler divergence is shown to
partially compensate for the effects of a non-uniform window function.
