Bayesian Statistical Inversion for High-Dimensional Computer Model Output and Spatially Distributed Counts

TL;DR

This paper introduces a novel Bayesian inversion framework combining Poisson responses with sparse Gaussian process surrogates to efficiently analyze high-dimensional computer model outputs and satellite count data, demonstrated on space physics data.

Contribution

The paper presents a new Bayesian inverse modeling approach that handles high-dimensional outputs and count data using sparse Gaussian processes and the Vecchia approximation.

Findings

Framework accurately recovers true model parameters

Outperforms alternative methods in simulations

Successfully applied to IBEX satellite data

Abstract

Data collected by the Interstellar Boundary Explorer (IBEX) satellite, recording heliospheric energetic neutral atoms (ENAs), exhibit a phenomenon that has caused space scientists to revise hypotheses about the physical processes, and computer simulations under those models, in play at the boundary of our solar system. Evaluating the fit of these computer models involves tuning their parameters to observational data from IBEX. This would be a classic (Bayesian) inverse problem if not for three challenges: (1) the computer simulations are slow, limiting the size of campaigns of runs; so (2) surrogate modeling is essential, but outputs are high-resolution images, thwarting conventional methods; and (3) IBEX observations are counts, whereas most inverse problem techniques assume Gaussian field data. To fill that gap we propose a novel approach to Bayesian inverse problems coupling a Poisson response with a sparse Gaussian process surrogate using the Vecchia approximation. We demonstrate the capabilities of our proposed framework, which compare favorably to alternatives, through multiple simulated examples in terms of recovering "true" computer model parameters and accurate out-of-sample prediction. We then apply this new technology to IBEX satellite data and associated computer models developed at Los Alamos National Laboratory.