Machine-learning interpolation of population-synthesis simulations to interpret gravitational-wave observations: a case study

Understand

We report on advances to interpret current and future gravitational-wave events in light of astrophysical simulations.

• A machine-learning emulator is trained on numerical population-synthesis predictions and inserted into a Bayesian hierarchical framework.
• In this case study, a modest but state-of-the-art suite of simulations of isolated binary stars is interpolated across two event parameters and one population parameter.
• The validation process of our pipelines highlights how omitting some of the event parameters might cause errors in estimating selection effects, which propagates as systematics to the final population inference.