Thesis
Multi-baseline gravitational wave radiometry
Washington State University
Master of Science (MS), Washington State University
2008
Handle:
https://hdl.handle.net/2376/102450
Abstract
We consider the maximum likelihood (ML) statistic for detecting an anisotropic astrophysical stochastic gravitational-wave background with multiple interferometric baselines. For any given baseline, we establish a formalism for constructing an orthonormal pixel basis in sky positions utilizing the knowledge of the point-spread function for that baseline. The ML statistic for a single baseline is then just the excess power in that orthonormal basis. An analogous formulation of the ML statistic is available for a spherical harmonic basis and lays the ground-work for a systematic comparison between the effectiveness of pixel-based and spherical-harmonic-based deconvolution techniques for a variety of stochastic source distributions. The sensitivities of three different baselines and their network for single- and multi-pixel sources are compared here. For detector noise that is Gaussian and uncorrelated across baselines, the network sensitivity-squared is the sum of the squares of the individual baseline sensitivities, analogous to what was found before for the network signal-to-noise ratio (SNR) of the "optimal filter" statistic for an isotropic stochastic gravitational wave background. Also, the accuracies with which a single-pixel source can be located with the separate baselines and their network are obtained and compared using the Fisher information matrix.
Metrics
2 File views/ downloads
10 Record Views
Details
- Title
- Multi-baseline gravitational wave radiometry
- Creators
- Dipongkar Talukder
- Contributors
- Sukanta Bose (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Physics and Astronomy, Department of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; Pullman, Wash. :
- Identifiers
- 99900525151401842
- Language
- English
- Resource Type
- Thesis