Cost minimization in acquisition for gravitational wave surrogate modeling

Abstract

Gravitational wave science is dependent upon expensive numerical simulations, which provide the foundational understanding of binary merger radiation needed to interpret observations of massive binary black holes. The high cost of these simulations limits large-scale campaigns to explore the binary black hole parameter space. Surrogate models have been developed to efficiently interpolate between simulation results, but these models require a sufficiently comprehensive sample to train on. Acquisition functions can be used to identify points in the domain for simulation. We develop a new acquisition function which accounts for the cost of simulating new points. We show that when applied to a 3D domain of binary mass ratio and dimensionless nonprecessing spins, the accumulated cost of simulation is reduced by a factor of about 10.