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

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1905.00910.pdf

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##### Citation

Broekgaarden, F. S., Justham, S., de Mink, S. E., Gair, J., Mandel, I., Stevenson, S., et al. (2019).
STROOPWAFEL: Simulating rare outcomes from astrophysical populations, with application to gravitational-wave sources.* Monthly notices of the Royal Astronomical Society,* *490*(4),
5228-5248. doi:10.1093/mnras/stz2558.

Cite as: https://hdl.handle.net/21.11116/0000-0003-A1C3-A

##### 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.

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.