J/A+A/624/A61 PySSC code (Lacasa+, 2019)
Fast and easy super-sample covariance of large scale structure observables.
Lacasa F., Grain J.
<Astron. Astrophys. 624, A61 (2019)>
=2019A&A...624A..61L 2019A&A...624A..61L (SIMBAD/NED BibCode)
ADC_Keywords: Models
Keywords: large-scale structure of the universe - galaxies: statistics -
methods: data analysis - methods: analytical
Description:
We present a numerically cheap approximation to super-sample
covariance (SSC) of large scale structure cosmological probes, first
in the case of angular power spectra. It necessitates no new elements
besides those used for the prediction of the considered probes, thus
relieving analysis pipelines from having to develop a full SSC
modeling, and reducing the computational load. The approximation is
asymptotically exact for fine redshift bins {Delat}z->0. We
furthermore show how it can be implemented at the level of a Gaussian
likelihood or a Fisher matrix forecast, as a fast correction to the
Gaussian case without needing to build large covariance matrices.
Numerical application to a Euclid-like survey show that, compared to a
full SSC computation, the approximation recovers nicely the
signal-to-noise ratio as well as Fisher forecasts on cosmological
parameters of the wCDM cosmological model. Moreover it allows for a
fast prediction of which parameters are going to be the most aected by
SSC and at which level. In the case of photometric galaxy clustering
with Euclid-like specifications, we find that 8, ns and the dark
energy equation of state w are particularly heavily aected. We finally
show how to generalize the approximation for probes other than angular
spectra (correlation functions, number counts and bispectra), and at
the likelihood level, allowing for the latter to be non-Gaussian if
needs be. We release publicly a Python module allowing to implement
the SSC approximation, as well as a notebook reproducing the plots of
the article, at https://github.com/fabienlacasa/PySSC
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
PySSC.tar 512 2116 PySSC code
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Acknowledgements:
Fabien Lacase, Fabien.Lacasa(at)unige.ch
(End) Patricia Vannier [CDS] 28-Mar-2019