# STROOPWAFEL: Simulating rare outcomes from astrophysical populations, with application to gravitational-wave sources

## TL;DR

The paper introduces STROOPWAFEL, an importance sampling algorithm that significantly enhances the efficiency of simulating rare astrophysical events like gravitational-wave sources, enabling higher resolution and reduced statistical noise in population studies.

## Contribution

STROOPWAFEL is a novel importance sampling method implemented in COMPAS that accelerates binary population synthesis by 25-200 times, improving the study of rare DCO merger events.

## Key findings

- STROOPWAFEL increases DCO merger detection by up to 200 times.
- Simulation speed improved by up to two orders of magnitude.
- Reduces statistical uncertainty in population predictions by a factor of 3-10.

## Abstract

Gravitational-wave observations of double compact object (DCO) mergers are providing new insights into the physics of massive stars and the evolution of binary systems. Making the most of expected near-future observations for understanding stellar physics will rely on comparisons with binary population synthesis models. However, the vast majority of simulated binaries never produce DCOs, which makes calculating such populations computationally inefficient. We present an importance sampling algorithm, STROOPWAFEL, that improves the computational efficiency of population studies of rare events, by focusing the simulation around regions of the initial parameter space found to produce outputs of interest. We implement the algorithm in the binary population synthesis code COMPAS, and compare the efficiency of our implementation to the standard method of Monte Carlo sampling from the birth probability distributions. STROOPWAFEL finds $\sim$25-200 times more DCO mergers than the standard sampling method with the same simulation size, and so speeds up simulations by up to two orders of magnitude. Finding more DCO mergers automatically maps the parameter space with far higher resolution than when using the traditional sampling. This increase in efficiency also leads to a decrease of a factor $\sim$3-10 in statistical sampling uncertainty for the predictions from the simulations. This is particularly notable for the distribution functions of observable quantities such as the black hole and neutron star chirp mass distribution, including in the tails of the distribution functions where predictions using standard sampling can be dominated by sampling noise.