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Hierarchical multi-stage MCMC follow-up of continuous gravitational wave candidates
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Leveraging Markov chain Monte Carlo (MCMC) optimization of the F-statistic, we introduce a method for the hierarchical follow-up of continuous gravitational wave candidates identified by wide-parameter space semi-coherent 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.
Forward citations
Cited by 2 Pith papers
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The G347.3-0.5 outlier from O3: a follow-up case study for continuous gravitational-wave candidates
Multi-pipeline follow-up recovers the G347.3-0.5 outlier in O3 data but finds no standard persistent continuous-wave signal in O4a or O4b, disfavouring the astrophysical interpretation under the assumed model.
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Applications of machine learning in gravitational wave research with current interferometric detectors
A community review of machine learning in current gravitational-wave detectors, mapping where ML already works in production (noise subtraction, glitch classification, low-latency classification) and where traditional...
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