http://arxiv.org/abs/2009.13812
The low frequency sensitivity on the orders of a few Hz in future gravitational-wave observatories will enable the detection of gravitational wave signals of very long duration. The runtime of parameter estimation with these long waveforms can be months even years, which make it impractical with existing Bayesian inference pipelines. Reduced order modelling and reduced order quadrature integration rules have recently been exploited as promising techniques that can greatly reduce parameter estimation computational costs. We describe a Python-based reduced order quadrature building code named PyROQ that builds the reduced order quadrature data needed to accelerate the parameter estimation of gravitational waves. The infrastructure of the code is also directly applicable to gravitational wave inference for space-borne gravitational-wave detectors such as the Laser Interferometer Space Antenna (LISA). In addition, the techniques are broadly applicable to other research fields where fast Bayesian analysis is necessary.
H. Qi and V. Raymond
Wed, 30 Sep 2020
78/86
Comments: 11 pages, 7 figures
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