Parameter Inference for Weak Lensing using Gaussian Processes and MOPED

Understand

In this paper, we propose a Gaussian Process (GP) emulator for the calculation of a) tomographic weak lensing band-power spectra, and b) coefficients of summary data massively compressed with the MOPED algorithm.

• In the former case cosmological parameter inference is accelerated by a factor of $\sim 10$-$30$ compared to explicit calls to the Boltzmann solver CLASS when applied to KiDS-450 weak lensing data.
• Much larger gains will come with future data, where with MOPED compression, the speed up can be up to a factor of $\sim 10^3$ when the common Limber approximation is used.
• Furthermore, the GP opens up the possibility of dropping the Limber approximation, without which the theoretical calculations may be unfeasibly slow.