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Accelerated gravitational wave parameter estimation with reduced order modeling.

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Canizares, Priscilla 
Field, Scott E 
Gair, Jonathan 
Raymond, Vivien 
Smith, Rory 


Inferring the astrophysical parameters of coalescing compact binaries is a key science goal of the upcoming advanced LIGO-Virgo gravitational-wave detector network and, more generally, gravitational-wave astronomy. However, current approaches to parameter estimation for these detectors require computationally expensive algorithms. Therefore, there is a pressing need for new, fast, and accurate Bayesian inference techniques. In this Letter, we demonstrate that a reduced order modeling approach enables rapid parameter estimation to be performed. By implementing a reduced order quadrature scheme within the LIGO Algorithm Library, we show that Bayesian inference on the 9-dimensional parameter space of nonspinning binary neutron star inspirals can be sped up by a factor of ∼30 for the early advanced detectors' configurations (with sensitivities down to around 40 Hz) and ∼70 for sensitivities down to around 20 Hz. This speedup will increase to about 150 as the detectors improve their low-frequency limit to 10 Hz, reducing to hours analyses which could otherwise take months to complete. Although these results focus on interferometric gravitational wave detectors, the techniques are broadly applicable to any experiment where fast Bayesian analysis is desirable.


This is the author accepted manuscript. The final version is available from APS via


5101 Astronomical Sciences, 51 Physical Sciences

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Phys Rev Lett

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American Physical Society (APS)
P. C.’s work was supported by a Marie Curie Intra-European Fellowship within the 7th European Community Framework Programme PIEF-GA-2011-299190, FP7-PEOPLE-2011-CIG Grant No. 293412 and STFC Consolidator Grant No. ST/L000636/1. S. E. F. thanks the Institute of Astronomy at Cambridge, UK, where part of this work was done, for hospitality and financial support. J. G.’s work was supported by the Royal Society and V. R. by the LIGO Laboratory and the California Institute of Technology (Caltech). This work was supported in part by NSF Grant No. PHY-1208861 and No. PHY-1316424 to the University of Maryland (UMD), NSF Grant No. PHY-1500818 to the University of California at San Diego, NSF Grants No. PHY-1306125 and No. AST-1333129 to Cornell University, and the Sherman Fairchild Foundation. The authors also gratefully acknowledge the support of the U.S. National Science Foundation for the construction and operation of the LIGO Laboratory under cooperative agreement NSF-PHY-0757058. This paper carries LIGO Document Number LIGO-P1400038. Some of the computations were carried out at the Center for Scientific Computation and Mathematical Modeling cluster at UMD and the LIGO Laboratory computer cluster at Caltech. Portions of this research were conducted with high performance computing resources provided by Louisiana State University