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An F-statistic based multi-detector veto for detector artifacts in continuous-wave gravitational wave data

MPS-Authors
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Keitel,  David
Laser Interferometry & Gravitational Wave Astronomy, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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

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Papa,  Maria Alessandra
Astrophysical Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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1201.5244
(Preprint), 30KB

chapter55.pdf
(Any fulltext), 130KB

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Citation

Keitel, D., Prix, R., Papa, M. A., & Siddiqi, M. (2012). An F-statistic based multi-detector veto for detector artifacts in continuous-wave gravitational wave data. In E. D. Feigelson, & G. J. Babu (Eds.), Statistical Challenges in Modern Astronomy V (pp. 511-513). Heidelberg u.a.: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-3D23-2
Abstract
Continuous gravitational waves (CW) are expected from spinning neutron stars with non-axisymmetric deformations. A network of interferometric detectors (LIGO, Virgo and GEO600) is looking for these signals. They are predicted to be very weak and retrievable only by integration over long observation times. One of the standard methods of CW data analysis is the multi-detector F-statistic. In a typical search, the F-statistic is computed over a range in frequency, spin-down and sky position, and the candidates with highest F values are kept for further analysis. However, this detection statistic is susceptible to a class of noise artifacts, strong monochromatic lines in a single detector. By assuming an extended noise model - standard Gaussian noise plus single-detector lines - we can use a Bayesian odds ratio to derive a generalized detection statistic, the line veto (LV-) statistic. In the absence of lines, it behaves similarly to the F-statistic, but it is more robust against line artifacts. In the past, ad-hoc post-processing vetoes have been implemented in searches to remove these artifacts. Here we provide a systematic framework to develop and benchmark this class of vetoes. We present our results from testing this LV-statistic on simulated data.