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  Density-clustering of continuous gravitational wave candidates from large surveys

Steltner, B., Menne, T., Papa, M. A., & Eggenstein, H.-B. (in preparation). Density-clustering of continuous gravitational wave candidates from large surveys.

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2207.14286.pdf (Preprint), 2MB
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 Creators:
Steltner, Benjamin1, Author           
Menne, Thorben1, Author           
Papa, Maria Alessandra1, Author           
Eggenstein, Heinz-Bernd2, Author           
Affiliations:
1Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, ou_2630691              
2Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, ou_24011              

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Free keywords: General Relativity and Quantum Cosmology, gr-qc, Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM
 Abstract: Searches for continuous gravitational waves target nearly monochromatic gravitational wave emission from e.g. non-axysmmetric fast-spinning neutron stars. Broad surveys often require to explicitly search for a very large number of different waveforms, easily exceeding $\sim10^{17}$ templates. In such cases, for practical reasons, only the top, say $\sim10^{10}$, results are saved and followed-up through a hierarchy of stages. Most of these candidates are not completely independent of neighbouring ones, but arise due to some common cause: a fluctuation, a signal or a disturbance. By judiciously clustering together candidates stemming from the same root cause, the subsequent follow-ups become more effective. A number of clustering algorithms have been employed in past searches based on iteratively finding symmetric and compact over-densities around candidates with high detection statistic values. The new clustering method presented in this paper is a significant improvement over previous methods: it is agnostic about the shape of the over-densities, is very efficient and it is effective: at a very high detection efficiency, it has a noise rejection of $99.99\%$ , is capable of clustering two orders of magnitude more candidates than attainable before and, at fixed sensitivity it enables more than a factor of 30 faster follow-ups. We also demonstrate how to optimally choose the clustering parameters.

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 Dates: 2022-07-28
 Publication Status: Not specified
 Pages: 7 pages, 3 figures
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 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 2207.14286
 Degree: -

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