http://arxiv.org/abs/1904.11070
To make use of clustering statistics from large cosmological surveys, accurate and precise covariance matrices are needed. We present a new code to estimate large scale galaxy correlation function covariances in arbitrary survey geometries that produces results comparable to a suite of $10^6$ mocks in $\sim 100$ CPU-hours, orders-of-magnitude faster than pre-existing codes. As in previous works, non-Gaussianity is encapsulated via a shot-noise rescaling, with calibrations performed by comparing models to jackknifed survey data. The approach requires data from only a single dataset (without an input correlation function model), and the deviations between large scale model covariances from a mock survey and those from a large suite of mocks are found to be be indistinguishable from noise. In addition, the choice of input mock are shown to be irrelevant for desired noise levels below $\sim 10^5$ mocks. Coupled with its generalization to multi-tracer data-sets, this shows the algorithm to be an excellent tool for analysis, reducing the need for large numbers of mock simulations to be computed.
O. Philcox, D. Eisenstein, R. O’Connell, et. al.
Fri, 26 Apr 19
50/69
Comments: 28 pages, 8 figures. Submitted to MNRAS. Code is available at this http URL with documentation at this http URL
You must be logged in to post a comment.