Massively parallel Bayesian inference for transient gravitationalwave astronomy
Abstract
Understanding the properties of transient gravitational waves (GWs) and their sources is of broad interest in physics and astronomy. Bayesian inference is the standard framework for astrophysical measurement in transient GW astronomy. Usually, stochastic sampling algorithms are used to estimate posterior probability distributions over the parameter spaces of models describing experimental data. The most physically accurate models typically come with a large computational overhead which can render data analsis extremely time consuming, or possibly even prohibitive. In some cases highly specialized optimizations can mitigate these issues, though they can be difficult to implement, as well as to generalize to arbitrary models of the data. Here, we investigate an accurate, flexible, and scalable method for astrophysical inference: parallelized nested sampling. The reduction in the walltime of inference scales almost linearly with the number of parallel processes running on a highperformance computing cluster. By utilizing a pool of several hundreds or thousands of CPUs in a highperformance cluster, the large wall times of many astrophysical inferences can be alleviated while simultaneously ensuring that any GW signal model can be used 'out of the box', i.e. without additional optimization or approximation. Our method will be useful to both the LIGOVirgoKAGRA collaborations and the wider scientific community performing astrophysical analyses on GWs. An implementation is available in the open source gravitationalwave inference library pBilby (parallel bilby).
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 August 2020
 DOI:
 10.1093/mnras/staa2483
 arXiv:
 arXiv:1909.11873
 Bibcode:
 2020MNRAS.498.4492S
 Keywords:

 gravitational waves;
 methods: data analysis;
 General Relativity and Quantum Cosmology;
 Astrophysics  Instrumentation and Methods for Astrophysics
 EPrint:
 9 pages, 2 figures, 1 table