Abstract

We discuss strategies for performing Bayesian uncertainty analyses for extremely expensive simulators. The EAGLE model is one of the most (arguably the most) complex hydrodynamical Galaxy formation simulations yet performed. It is however extremely expensive, currently taking approximately 5 million hours of CPU time, with order of magnitude increases in runtime planned. This makes a full uncertainty analysis involving the exploration of multiple input parameters along with several additional uncertainty assessments, seemingly impossible. We present a strategy for the resolution of this problem, which incorporates four versions of the EAGLE model, of varying speed and accuracy, within a specific multilevel emulation framework that facilitates the incorporation of detailed judgements regarding the uncertain links between the physically different model versions. We show how this approach naturally fits within the iterative history matching process, whereby regions of input parameter space are identified that may lead to acceptable matches between model output and the real universe, given all major sources of uncertainty. We will briefly discuss the detailed assessment of such uncertainties as observation error and structural model discrepancies and their various components, and emphasise that without such assessments any such analysis rapidly loses meaning.