publication . Preprint . 2014

A sparse representation of gravitational waves from precessing compact binaries

Blackman, Jonathan; Szilagyi, Bela; Galley, Chad R.; Tiglio, Manuel;
Open Access English
  • Published: 27 Jan 2014
Abstract
Comment: 5 pages, 3 figures. The parameters selected for the basis of precessing waveforms can be found in the source files
Subjects
free text keywords: General Relativity and Quantum Cosmology
Funded by
NSF| Collaborative Research: Developing Spectral Methods for Numerical Solution of Einstein's Equations
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1005655
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Physics
,
NSF| Algorithms and Scientific Computing for Gravitational Waves Source Modeling
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1005632
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Physics
,
NSF| CAREER: Gravitational-Wave Science, from Macroscopic Quantum Mechanics to Strong-Field General Relativity
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 0956189
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Physics
,
NSF| Gravitational Radiation and Relativistic Astrophysics
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1068881
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Physics
,
NSF| MRI-R2: Acquisition of a Compute Cluster for High-Fidelity Simulations of Gravitational Wave Sources -- Facilitating LIGO and Enabling Multi-Messenger Astronomy
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 0960291
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Physics
Download from
38 references, page 1 of 3

[1] LIGO Scienti c Collaboration, G. M. Harry, Class.Quant.Grav. 27, 084006 (2010).

[2] LIGO Scienti c Collaboration, VIRGO Collaboration, G. Losurdo, J.Phys.Conf.Ser. 110, 062016 (2008).

[3] KAGRA Collaboration, K. Somiya, Class.Quant.Grav. 29, 124007 (2012), 1111.7185.

[4] IndIGO - http://www.gw-indigo.org.

[5] Gravitational wave detectors have a nite frequency bandwidth that introduces a total mass scale, thus adding an 8th parameter that we will ignore. We also focus on quasi-circular inspirals.

[6] M. Hannam, (2013), 1312.3641.

[7] R. Bellman, Adaptive Control Process: A Guided Tour (Princeton University Press, 1961).

[8] S. E. Field et al., Phys. Rev. Lett. 106, 221102 (2011), 1101.3765.

[9] S. E. Field, C. R. Galley, and E. Ochsner, Phys. Rev. D86, 084046 (2012), 1205.6009.

[10] S. Caudill, S. E. Field, C. R. Galley, F. Herrmann, and M. Tiglio, Class. Quant. Grav. 29, 095016 (2012), 1109.5642.

[11] S. E. Field, C. R. Galley, J. S. Hesthaven, J. Kaye, and M. Tiglio, (2013), 1308.3565.

[12] P. Binev et al., SIAM J. Math. Analysis 43, 1457 (2011).

[13] R. DeVore, G. Petrova, and P. Wojtaszczyk, Constructive Approximation 37, 455 (2013).

[14] C. R. Galley, F. Herrmann, J. Silberholz, M. Tiglio, and G. Guerbero , Class. Quantum Grav. 27, 245007 (2010), 1005.5560.

[15] The last two ingredients can be viewed as aspects of nonlinear dimensional reduction and manifold learning, which aim to reveal the intrinsic dimensionality of large amounts of data (e.g., see [38]).

38 references, page 1 of 3
Abstract
Comment: 5 pages, 3 figures. The parameters selected for the basis of precessing waveforms can be found in the source files
Subjects
free text keywords: General Relativity and Quantum Cosmology
Funded by
NSF| Collaborative Research: Developing Spectral Methods for Numerical Solution of Einstein's Equations
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1005655
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Physics
,
NSF| Algorithms and Scientific Computing for Gravitational Waves Source Modeling
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1005632
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Physics
,
NSF| CAREER: Gravitational-Wave Science, from Macroscopic Quantum Mechanics to Strong-Field General Relativity
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 0956189
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Physics
,
NSF| Gravitational Radiation and Relativistic Astrophysics
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1068881
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Physics
,
NSF| MRI-R2: Acquisition of a Compute Cluster for High-Fidelity Simulations of Gravitational Wave Sources -- Facilitating LIGO and Enabling Multi-Messenger Astronomy
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 0960291
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Physics
Download from
38 references, page 1 of 3

[1] LIGO Scienti c Collaboration, G. M. Harry, Class.Quant.Grav. 27, 084006 (2010).

[2] LIGO Scienti c Collaboration, VIRGO Collaboration, G. Losurdo, J.Phys.Conf.Ser. 110, 062016 (2008).

[3] KAGRA Collaboration, K. Somiya, Class.Quant.Grav. 29, 124007 (2012), 1111.7185.

[4] IndIGO - http://www.gw-indigo.org.

[5] Gravitational wave detectors have a nite frequency bandwidth that introduces a total mass scale, thus adding an 8th parameter that we will ignore. We also focus on quasi-circular inspirals.

[6] M. Hannam, (2013), 1312.3641.

[7] R. Bellman, Adaptive Control Process: A Guided Tour (Princeton University Press, 1961).

[8] S. E. Field et al., Phys. Rev. Lett. 106, 221102 (2011), 1101.3765.

[9] S. E. Field, C. R. Galley, and E. Ochsner, Phys. Rev. D86, 084046 (2012), 1205.6009.

[10] S. Caudill, S. E. Field, C. R. Galley, F. Herrmann, and M. Tiglio, Class. Quant. Grav. 29, 095016 (2012), 1109.5642.

[11] S. E. Field, C. R. Galley, J. S. Hesthaven, J. Kaye, and M. Tiglio, (2013), 1308.3565.

[12] P. Binev et al., SIAM J. Math. Analysis 43, 1457 (2011).

[13] R. DeVore, G. Petrova, and P. Wojtaszczyk, Constructive Approximation 37, 455 (2013).

[14] C. R. Galley, F. Herrmann, J. Silberholz, M. Tiglio, and G. Guerbero , Class. Quantum Grav. 27, 245007 (2010), 1005.5560.

[15] The last two ingredients can be viewed as aspects of nonlinear dimensional reduction and manifold learning, which aim to reveal the intrinsic dimensionality of large amounts of data (e.g., see [38]).

38 references, page 1 of 3
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