Document Type
Article
Publication Date
2010
Abstract
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.
Recommended Citation
Wang, Yan, and Soumya D. Mohanty. “Particle Swarm Optimization and Gravitational Wave Data Analysis: Performance on a Binary Inspiral Testbed.” Physical Review D, vol. 81, no. 6, American Physical Society, Mar. 2010, p. 063002, doi:10.1103/PhysRevD.81.063002.
Publication Title
Physical Review D
DOI
10.1103/PhysRevD.81.063002
Comments
© 2010 American Physical Society. Original published version available at https://doi.org/10.1103/PhysRevD.81.063002