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Optimal directed searches for continuous gravitational waves

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Ming,  Jing
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;
Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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Krishnan,  Badri
Astrophysical Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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Papa,  Maria Alessandra
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;
Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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Aulbert,  Carsten
Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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Fehrmann,  Henning
Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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1510.03417.pdf
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Citation

Ming, J., Krishnan, B., Papa, M. A., Aulbert, C., & Fehrmann, H. (2016). Optimal directed searches for continuous gravitational waves. Physical Review D, 93: 064011. doi:10.1103/PhysRevD.93.064011.


Cite as: https://hdl.handle.net/11858/00-001M-0000-002A-718D-0
Abstract
Wide parameter space searches for long lived continuous gravitational wave signals are computationally limited. It is therefore critically important that available computational resources are used rationally. In this paper we consider directed searches, i.e. targets for which the sky position is known accurately but the frequency and spindown parameters are completely unknown. Given a list of such potential astrophysical targets, we therefore need to prioritize. On which target(s) should we spend scarce computing resources? What parameter space region in frequency and spindown should we search? Finally, what is the optimal search set-up that we should use? In this paper we present a general framework that allows to solve all three of these problems. This framework is based on maximizing the probability of making a detection subject to a constraint on the maximum available computational cost. We illustrate the method for a simplified problem.