English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Fast and Accurate Inference on Gravitational Waves from Precessing Compact Binaries

MPS-Authors
/persons/resource/persons192115

Pürrer,  Michael
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

/persons/resource/persons192117

Raymond,  Vivien
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)

1604.08253.pdf
(Preprint), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Smith, R., Field, S. E., Blackburn, K., Haster, C.-J., Pürrer, M., Raymond, V., et al. (2016). Fast and Accurate Inference on Gravitational Waves from Precessing Compact Binaries. Physical Review D, 94: 044031. doi:10.1103/PhysRevD.94.044031.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002A-7104-4
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 of inference by removing redundant computations. In this paper we construct the first reduced order models of gravitational-wave signals that include the effects of spin-precession, inspiral, merger, and ringdown in compact object binaries, and which are valid for component masses describing binary neutron star, binary black hole and mixed binary systems. This work utilizes the waveform model known as "IMRPhenomPv2". Our ROM enables the use of a fast \textit{reduced order quadrature} (ROQ) integration rule which allows us to approximate Bayesian probability density functions at a greatly reduced computational cost. We find that the ROQ rule can be used to speed up inference by factors as high as 300 without introducing systematic bias. This corresponds to a reduction in computational time from around half a year to a half a day, for the longest duration/lowest mass signals. The ROM and ROQ rule are available with the main inference library of the LIGO Scientific Collaboration, LALInference.