Abstract Covariance matrix estimation is a challenging problem in cosmology. Recent work has shown that model covariance matrices can be precise, and that at relatively large scales they can also be accurate. We introduce a data-driven method that can identify features from a mock covariance matrix that are missing from a corresponding model, then incorporate them into the model without significantly degrading the model's precision. We apply this method to a BOSS-like survey and extend a model covariance to be valid at scales relevant for measurements of Redshift Space Distortions (8-40 Mpc/h), where the galaxy field is significantly non-Gaussian.
@article{arxiv.1911.04670,
title = {Residual Smoothing: Using Mocks to Correct Model Covariance Matrices},
author = {Ross O'Connell},
journal= {arXiv preprint arXiv:1911.04670},
year = {2019}
}