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rascalc : a jackknife approach to estimating single- and multitracer galaxy covariance matrices

Journal Article · · Monthly Notices of the Royal Astronomical Society
 [1];  [1];  [2];  [3]
  1. Harvard–Smithsonian Center for Astrophysics, 60 Garden St, Cambridge, MA 02138, USA
  2. McWilliams Center for Cosmology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
  3. Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str 1, D-85741 Garching, Germany
+ Show Author Affiliations
ABSTRACT

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 two-point correlation function (2PCF) covariances in arbitrary survey geometries that, due to new sampling techniques, runs ∼104 times faster than previous codes, computing finely binned covariance matrices with negligible noise in less than 100 CPU-hours. As in previous works, non-Gaussianity is approximated via a small rescaling of shot noise in the theoretical model, calibrated by comparing jackknife survey covariances to an associated jackknife model. The flexible code, rascalc, has been publicly released, and automatically takes care of all necessary pre- and post-processing, requiring only a single input data set (without a prior 2PCF model). Deviations between large-scale model covariances from a mock survey and those from a large suite of mocks are found to be indistinguishable from noise. In addition, the choice of input mock is shown to be irrelevant for desired noise levels below ∼105 mocks. Coupled with its generalization to multitracer 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.

Research Organization:
Harvard Univ., Cambridge, MA (United States)
Sponsoring Organization:
USDOE; USDOE Office of Science (SC)
Grant/Contract Number:
SC0013718
OSTI ID:
1734392
Alternate ID(s):
OSTI ID: 1802524
Journal Information:
Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 3 Vol. 491; ISSN 0035-8711
Publisher:
Oxford University PressCopyright Statement
Country of Publication:
United Kingdom
Language:
English

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Related Subjects

79 ASTRONOMY AND ASTROPHYSICS
Astronomy & Astrophysics
cosmology: theory
galaxies: statistics
large-scale structure of Universe
methods: numerical
methods: statistical