Particle swarm optimization and gravitational wave data analysis: Performance on a binary inspiral testbed
- Department of Astronomy, Nanjing University, Nanjing, 210093 (China)
The detection and estimation of gravitational wave signals belonging to a parameterized family of waveforms requires, in general, the numerical maximization of a data-dependent function of the signal parameters. Because of noise in the data, the function to be maximized is often highly multimodal with numerous local maxima. Searching for the global maximum then becomes computationally expensive, which in turn can limit the scientific scope of the search. Stochastic optimization is one possible approach to reducing computational costs in such applications. We report results from a first investigation of the particle swarm optimization method in this context. The method is applied to a test bed motivated by the problem of detection and estimation of a binary inspiral signal. Our results show that particle swarm optimization works well in the presence of high multimodality, making it a viable candidate method for further applications in gravitational wave data analysis.
- OSTI ID:
- 21413404
- Journal Information:
- Physical Review. D, Particles Fields, Vol. 81, Issue 6; Other Information: DOI: 10.1103/PhysRevD.81.063002; (c) 2010 The American Physical Society; ISSN 0556-2821
- Country of Publication:
- United States
- Language:
- English
Similar Records
Higher-order spin effects in the amplitude and phase of gravitational waveforms emitted by inspiraling compact binaries: Ready-to-use gravitational waveforms
Search of S3 LIGO data for gravitational wave signals from spinning black hole and neutron star binary inspirals