Uncertainty Quantification of a Computer Model for Binary Black Hole Formation

TL;DR

This paper presents a fast, parallelizable Gaussian Process-based emulator for binary black hole formation models, addressing unknown initial conditions and large data scales, enabling uncertainty propagation and simulation planning.

Contribution

Introduces a local GP-based emulator combining classification and regression for binary black hole formation, with a sequential design criterion for efficient simulation planning.

Findings

Effective uncertainty propagation of physical parameters.

Supports sequential design for simulation efficiency.

Handles large-scale simulation data efficiently.

Abstract

In this paper, a fast and parallelizable method based on Gaussian Processes (GPs) is introduced to emulate computer models that simulate the formation of binary black holes (BBHs) through the evolution of pairs of massive stars. Two obstacles that arise in this application are the a priori unknown conditions of BBH formation and the large scale of the simulation data. We address them by proposing a local emulator which combines a GP classifier and a GP regression model. The resulting emulator can also be utilized in planning future computer simulations through a proposed criterion for sequential design. By propagating uncertainties of simulation input through the emulator, we are able to obtain the distribution of BBH properties under the distribution of physical parameters.