Multifidelity emulation for the matter power spectrum using Gaussian processes

We present methods for emulating the matter power spectrum by combining information from cosmological N-body simulations at different resolutions. An emulator allows estimation of simulation output by interpolating across the parameter space of a limited number of simulations. We present the first implementation in cosmology of multifidelity emulation, where many low-resolution simulations are combined with a few high-resolution simulations to achieve an increased emulation accuracy. The power spectrum's dependence on cosmology is learned from the low-resolution simulations, which are in turn calibrated using high-resolution simulations. We show that our multifidelity emulator predicts high-fidelity (HF) counterparts to percent-level relative accuracy when using only three HF simulations and outperforms a single-fidelity emulator that uses 11 simulations, although we do not attempt to produce a converged emulator with high absolute accuracy. With a fixed number of HF training simulations, we show that our multifidelity emulator is similar or equal to 100 times better than a single-fidelity emulator at k <= 2 hMpc(-1), and similar or equal to 20 times better at 3 <= k < 6.4 hMpc(-1). Multifidelity emulation is fast to train, using only a simple modification to standard Gaussian processes. Our proposed emulator shows a new way to predict non-linear scales by fusing simulations from different fidelities.