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#### Optimal directed searches for continuous gravitational waves

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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.

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.