2016
DOI: 10.1103/physrevd.94.044031
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Fast and accurate inference on gravitational waves from precessing compact binaries

Abstract: Inferring astrophysical information from gravitational waves emitted by compact binaries is one of the key science goals of gravitational-wave astronomy. In order to reach the full scientific potential of gravitational-wave experiments, we require techniques to mitigate the cost of Bayesian inference, especially as gravitational-wave signal models and analyses become increasingly sophisticated and detailed. Reduced-order models (ROMs) of gravitational waveforms can significantly reduce the computational cost o… Show more

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Cited by 161 publications
(224 citation statements)
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“…This suggests that a hybrid model where the numerical or the analytical SUA is called depending on the system's mass can achieve both high accuracy and numerical efficiency. Further improvement could be obtained through reduced order modeling and reduced order quadrature integration in data analysis implementations [69]. We leave such studies to future work.…”
Section: Discussionmentioning
confidence: 99%
“…This suggests that a hybrid model where the numerical or the analytical SUA is called depending on the system's mass can achieve both high accuracy and numerical efficiency. Further improvement could be obtained through reduced order modeling and reduced order quadrature integration in data analysis implementations [69]. We leave such studies to future work.…”
Section: Discussionmentioning
confidence: 99%
“…The calculation is carried out using LAL-Inference. We employ reduced order modeling and reduced order quadrature methods to control the computational cost of the analysis [27].…”
Section: Demonstration Using Gaussian Noisementioning
confidence: 99%
“…Figure 4 implies that, at design sensitivity, a realistic astrophysical background with an effective z < 0.77 duty cycle of (ξ true = 4 × 10 −4 ) could be detected using ≈ 17000 4 s data segments. At the time of writing, a typical run-time of LALInference on 4 s data segments is usually no more than 10 CPU hours [27]. To produce evi-dences in a two detector network, we perform three separate runs: a coherent analysis which produces (Z i S , Z i N ); and two incoherent analysis which produce (Z i,(j) S , Z i,(j) N ) where the index j labels the detector, j = (1, 2).…”
Section: Time To Detectionmentioning
confidence: 99%
“…We use nested sampling implementation of LALInference [75] to stochastically explore the parameter space and produce posterior distributions for θ. To reduce the computational cost of the likelihood evaluations, we use the reduced order quadrature (ROQ) approximation [78]. Sampling the parameter space to measure the properties of weak signals in presence of strong priors can be challenging.…”
Section: B Bayesian Parameter Estimationmentioning
confidence: 99%