Uncertainty-aware and Data-efficient Cosmological Emulation using Gaussian Processes and PCA

Understand

Bayesian parameter inference is one of the key elements for model selection in cosmological research.

• However, the available inference tools require a large number of calls to simulation codes which can lead to high and sometimes even infeasible computational costs.
• In this work we propose a new way of emulating simulation codes for Bayesian parameter inference.
• In particular, this novel approach emphasizes the uncertainty-awareness of the emulator, which allows to state the emulation accuracy and ensures reliable performance.