Hierarchical multistage MCMC follow-up of continuous gravitational wave candidates
Hierarchical multistage MCMC follow-up of continuous gravitational wave candidates
Leveraging Markov chain Monte Carlo optimization of the F statistic, we introduce a method for the hierarchical follow-up of continuous gravitational wave candidates identified by wide-parameter space semicoherent searches. We demonstrate parameter estimation for continuous wave sources and develop a framework and tools to understand and control the effective size of the parameter space, critical to the success of the method. Monte Carlo tests of simulated signals in noise demonstrate that this method is close to the theoretical optimal performance.
Ashton, G.
a8cec4b1-3c98-4b28-af2a-1e37cb3b9f2a
Prix, R.
96ea3047-4071-44f4-9a9f-e9e331c61421
30 May 2018
Ashton, G.
a8cec4b1-3c98-4b28-af2a-1e37cb3b9f2a
Prix, R.
96ea3047-4071-44f4-9a9f-e9e331c61421
Ashton, G. and Prix, R.
(2018)
Hierarchical multistage MCMC follow-up of continuous gravitational wave candidates.
Physical Review D, 97 (10).
(doi:10.1103/PhysRevD.97.103020).
Abstract
Leveraging Markov chain Monte Carlo optimization of the F statistic, we introduce a method for the hierarchical follow-up of continuous gravitational wave candidates identified by wide-parameter space semicoherent searches. We demonstrate parameter estimation for continuous wave sources and develop a framework and tools to understand and control the effective size of the parameter space, critical to the success of the method. Monte Carlo tests of simulated signals in noise demonstrate that this method is close to the theoretical optimal performance.
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Published date: 30 May 2018
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Publisher Copyright: © 2018 American Physical Society.
M1 - 103020
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Local EPrints ID: 508007
URI: http://eprints.soton.ac.uk/id/eprint/508007
ISSN: 2470-0010
PURE UUID: 97515f83-49e0-4e87-81b8-58b14f5e9554
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Date deposited: 09 Jan 2026 17:44
Last modified: 10 Jan 2026 05:27
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Author:
G. Ashton
Author:
R. Prix
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