Massively parallel Bayesian inference for transient gravitational-wave astronomy

By Rory J. E. Smith, G. Ashton, A. Vajpeyi, Colm Talbot. Monthly notices of the Royal Astronomical Society, 2020

172 citations recorded by the source corpus.

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 wall-time of inference scales almost linearly with the number of parallel processes running on a high-performance computing cluster. By utilizing a pool of several hundreds or thousands of CPUs in a high-performance 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 LIGO-Virgo-KAGRA collaborations and the wider scientific community performing astrophysical analyses on GWs. An implementation is available in the open source gravitational-wave inference library pBilby (parallel bilby).

Subfields: Astronomy and Astrophysics, Oceanography

View Paper: https://www.semanticscholar.org/paper/13785a7aef7efd245794edf02c61d2ada6ffdbf3

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Citations in the corpus

Observation of Gravitational Waves from the Coalescence of a 2.5–4.5 M ⊙ Compact Object and a Neutron Star (2024, methodology): “We use the Bilby (Ashton et al. 2019; Romero-Shaw et al. 2020) or Parallel Bilby (Smith et al. 2020) inference libraries to generate samples from the posterior distribution of the source parameters us-ing a nested sampling (Skilling 2006) algorithm, as implemented in the Dynesty software package (Sp”
GW190521: Orbital Eccentricity and Signatures of Dynamical Formation in a Binary Black Hole Merger Signal (2020, methodology): “We use the Bayesian inference library BILBY (Ashton et al. 2019; Romero-Shaw et al. 2020) to perform an analysis using our “proposal” model: the spinaligned quasi-circular-waveform model IMRPHENOMD (Khan et al. 2016).”
GW231123: A Binary Black Hole Merger with Total Mass 190–265 M⊙ (2025, unknown): “Source-parameter estimation was primarily performed with the Bilby and BilbyPipe libraries (Ashton et al. 2019; Smith et al. 2020; Romero-Shaw et al. 2020b) using the Dynesty nested sampling package (Spea-gle 2020).”