Description
The probability density function of convergence maps (the lensing PDF) has been demonstrated to improve the precision of cosmological constraints over standard two-point (2pt) statistics by tens of percent. I will demonstrate how the cosmological dependence and impact of survey systematics on this statistic can be forward modelled using Gaussian process regression trained on N-body simulations. In particular, I will illustrate how intrinsic alignments, baryonic feedback, redshift distribution uncertainties, survey geometry, and galaxy shape noise can all be accounted for without sacrificing the gains in constraining power offered by this novel cosmic shear statistic. This work, undertaken with real data from the Kilo-Degree Survey (KiDS), sets the stage for overcoming the barriers posed by systematics in future beyond-2pt analyses with Stage-IV surveys such as Euclid and LSST.
