http://arxiv.org/abs/1909.11873
Understanding the properties of transient gravitational waves and their sources is of broad interest in physics and astronomy. Extracting information from gravitational waves involves comparing the signals and noise in experimental data to models of signals and noise . The most physically accurate models typically come with a large computational overhead which can render data analysis 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 propose a general solution to the large run time of astrophysical inference using a parallelized nested sampling algorithm. The reduction in the run-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 hundred CPUs in a high-performance cluster, the large analysis times of many astrophysical inferences can be alleviated while simultaneously ensuring that any gravitational-wave 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 gravitational-wave transients.
R. Smith and G. Ashton
Fri, 27 Sep 19
51/64
Comments: 6 pages, 1 table
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