Abstract Number:
2947
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Stephen Walsh (1), Annie Booth (2), David Higdon (3), Marco Ferreira (3)
Institutions:
(1) Elms College, N/A, (2) NC State University, N/A, (3) Virginia Tech, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
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.
Keywords:
Gaussian processes|Bayesian modeling|Hierarchical modeling|Deep Gaussian processes|Cosmology|
Sponsors:
Section on Bayesian Statistical Science
Tracks:
Applications in Applied Sciences
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