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Density-clustering of continuous gravitational wave candidates from large surveys

MPS-Authors
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Steltner,  Benjamin
Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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Menne,  Thorben
Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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Papa,  Maria Alessandra
Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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Eggenstein,  Heinz-Bernd
Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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2207.14286.pdf
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Citation

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


Cite as: https://hdl.handle.net/21.11116/0000-000A-CCEC-7
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.