Projected two- and three-point statistics: Forecasts and mitigation of
  non-linear RSDs
  The combination of two- and three-point clustering statistics of galaxies and
the underlying matter distribution has the potential to break degeneracies
between cosmological parameters and nuisance parameters and can lead to
significantly tighter constraints on parameters describing the composition of
the Universe and the dynamics of inflation. Here we investigate the relation
between biases in the estimated parameters and inaccurate modelling of
non-linear redshift-space distortions for the power spectrum and bispectrum of
projected galaxy density fields and lensing convergence. Non-linear
redshift-space distortions are one of the leading systematic uncertainties in
galaxy clustering. Projections along the line of sight suppress radial modes
and are thus allowing a trade-off between biases due to non-linear
redshift-space distortions and statistical uncertainties. We investigate this
bias-error trade-off for a CMASS-like survey with a varying number of redshift
bins. Improved modelling of the non-linear redshift-space distortions allows
the recovery of more radial information when controlling for biases. Not
modelling non-linear redshift space distortions inflates error bars for almost
all parameters by 20%. The information loss for the amplitude of local
non-Gaussianities is smaller, since it is best constrained from large scales.
In addition, we show empirically that one can recover more than 99% of the 3D
power spectrum information if the depth of the tomographic bins is reduced to
10 $h^{-1}$Mpc.
