# An adaptive clustering procedure for continuous gravitational wave searches

@article{Singh2017AnAC, title={An adaptive clustering procedure for continuous gravitational wave searches}, author={Avneet Singh and Maria Alessandra Papa and Heinz-Bernd Eggenstein and Sin'ead Walsh}, journal={Physical Review D}, year={2017}, volume={96}, pages={082003} }

In hierarchical searches for continuous gravitational waves, clustering of candidates is an important postprocessing step because it reduces the number of noise candidates that are followed-up at successive stages [1][7][12]. Previous clustering procedures bundled together nearby candidates ascribing them to the same root cause (be it a signal or a disturbance), based on a predefined cluster volume. In this paper, we present a procedure that adapts the cluster volume to the data itself and…

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## 16 Citations

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A deep learning network is implemented that identifies clusters of signal candidates in the output of continuous gravitational wave searches and achieves a detection efficiency higher than 97% with a very low false alarm rate.

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