We explore two methods of compressing the redshift space galaxy power
spectrum and bispectrum with respect to a chosen set of cosmological
parameters. Both methods involve reducing the dimension of the original
data-vector ( e.g. 1000 elements ) to the number of cosmological parameters
considered ( e.g. seven ) using the Karhunen-Lo\`eve algorithm. In the first
case, we run MCMC sampling on the compressed data-vector in order to recover
the one-dimensional (1D) and two-dimensional (2D) posterior distributions. The
second option, approximately 2000 times faster, works by orthogonalising the
parameter space through diagonalisation of the Fisher information matrix before
the compression, obtaining the posterior distributions without the need of MCMC
sampling. Using these methods for future spectroscopic redshift surveys like
DESI, EUCLID and PFS would drastically reduce the number of simulations needed
to compute accurate covariance matrices with minimal loss of constraining
power. We consider a redshift bin of a DESI-like experiment. Using the power
spectrum combined with the bispectrum as a data-vector, both compression
methods on average recover the 68% credible regions to within 0.7% and 2% of
those resulting from standard MCMC sampling respectively. These confidence
intervals are also smaller than the ones obtained using only the power spectrum
by (81%, 80%, 82%) respectively for the bias parameter b_1, the growth rate f
and the scalar amplitude parameter A_s.Comment: 27 pages, 8 figures, 1 table, Accepted 2018 January 28. Received 2018
January 25; in original form 2017 September 11. Added clarifications in the
text on the bias modelling and compression limits following referee's
comments. Removed tetraspectrum term from the pk-bk cross covariance +
correction in the appendi
