Year: 2019
Type: preprint
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.
Source: arXiv (Cornell University)
Author: O'Connell, Ross
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2985503606