Emulating Functional Output of Dark Matter Power Spectra Using Deep Gaussian Processes

Oregon Convention Center 

We construct a framework combining Gaussian processes and hierarchical modeling to estimate and emulate dark matter power spectra from multiple, dependent computer model simulations. We model the spectra as deep Gaussian processes, and consider multiple candidate models for the covariance structure of the simulations' deviations from the true spectra. Applying the best candidate model to the expensive simulations, we estimate the underlying power spectrum for a given cosmology. With these estimates calculated across multiple cosmologies, we build an emulator using functional principal components (and Gaussian processes on the weights) for unobserved cosmologies. We obtain promising results comparing against an existing method.

Gaussian processes

Bayesian modeling

Hierarchical modeling

Deep Gaussian processes

Cosmology 

Section on Bayesian Statistical Science