Uncertainty quantification of a computer model for binary black hole formation

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 evolution pairs massive stars. Two obstacles arise in application are priori unknown conditions BBH large scale simulation data. We address them by proposing local emulator which combines GP classifier regression model. The resulting can also be utilized planning future simulations proposed criterion for sequential design. By propagating uncertainties input emulator, we able obtain distribution properties under physical parameters.