Optimised interpolation of astrophysical simulations: Bayesian emulator optimisation
In order to test astrophysical theories, we must compare data to accurate modelling of our observations -- i.e., we must evaluate the likelihood function. However, this is often impossible because the necessary astrophysical simulations are too computationally expensive for the millions of likelihood samples we need (e.g., using MCMC methods). I will present a solution which combines Gaussian process emulation with Bayesian optimisation of the training set. Here, a small training set of simulations is used to predict simulation outputs throughout parameter space using a Gaussian process model (thus allowing evaluation of the likelihood). This is made accurate and efficient using Bayesian optimisation to build up the training set, conditional on the information already learnt and the uncertainty present from previous iterations of the training data. I will demonstrate an application of cosmological inference from the Lyman-alpha forest (a spectroscopic probe of the intergalactic medium at redshifts 2 to 6) using hydrodynamical simulations.