Kavli Affiliate: Katrin Heitmann
| First 5 Authors: Stephen A. Walsh, Stephen A. Walsh, , ,
| Summary:
Understanding the structure of our universe and the distribution of matter is
an area of active research. As cosmological surveys grow in complexity, the
development of emulators to efficiently and effectively predict matter power
spectra is essential. We are particularly motivated by the Mira-Titan Universe
simulation suite that, for a specified cosmological parameterization (termed a
"cosmology"), provides multiple response curves of various fidelities,
including correlated functional realizations. Our objective is two-fold. First,
we estimate the underlying true matter power spectra, with appropriate
uncertainty quantification (UQ), from all of the provided curves. To this end,
we propose a novel Bayesian deep Gaussian process (DGP) hierarchical model
which synthesizes all the simulation information to estimate the underlying
matter power spectra while providing effective UQ. Our model extends previous
work on Bayesian DGPs from scalar responses to correlated functional outputs.
Second, we leverage our predicted power spectra from various cosmologies in
order to accurately predict the entire matter power spectra for an unobserved
cosmology. For this task, we use basis function representations of the
functional spectra to train a separate Gaussian process emulator. Our method
performs well in synthetic exercises and against the benchmark cosmological
emulator (Cosmic Emu).
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