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

Broekgaarden, F. S., Justham, S., de Mink, S. E., Gair, J., Mandel, I., Stevenson, S., et al. (in preparation). STROOPWAFEL: Simulating rare outcomes from astrophysical populations, with application to gravitational-wave sources.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-A1C3-A Version Permalink: http://hdl.handle.net/21.11116/0000-0003-A1C6-7
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 Creators:
Broekgaarden, Floor S., Author
Justham, Stephen, Author
de Mink, Selma E., Author
Gair, Jonathan1, Author              
Mandel, Ilya, Author
Stevenson, Simon, Author
Barrett, Jim W., Author
Vigna-Gómez, Alejandro, Author
Neijssel, Coenraad2, Author              
Affiliations:
1Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society, ou_1933290              
2Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, ou_24011              

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Free keywords: Astrophysics, High Energy Astrophysical Phenomena, astro-ph.HE, Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM, Astrophysics, Solar and Stellar Astrophysics, astro-ph.SR, Physics, Data Analysis, Statistics and Probability, physics.data-an
 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.

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 Dates: 2019-05-02
 Publication Status: Not specified
 Pages: Submitted. Data and the code for the STROOPWAFEL algorithm will be made publicly available after acceptance. Early inquiries can be addressed to the lead author
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: arXiv: 1905.00910
URI: http://arxiv.org/abs/1905.00910
 Degree: -

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