Journal article
Detecting a stochastic gravitational-wave background in the presence of correlated magnetic noise
PM Meyers, K Martinovic, N Christensen, M Sakellariadou
Physical Review D | AMER PHYSICAL SOC | Published : 2020
Abstract
A detection of the stochastic gravitational-wave background (SGWB) from unresolved compact binary coalescences could be made by Advanced LIGO and Advanced Virgo at their design sensitivities. However, it is possible for magnetic noise that is correlated between spatially separated ground-based detectors to mimic a SGWB signal. In this paper we propose a new method for detecting correlated magnetic noise and separating it from a true SGWB signal. A commonly discussed method for addressing correlated magnetic noise is coherent subtraction in the raw data using Wiener filtering. The method proposed here uses a parametrized model of the magnetometer-to-strain coupling functions, along with measu..
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Grants
Awarded by King’s College London
Funding Acknowledgements
The authors would like to thank Thomas Callister, Giancarlo Cella, and the LIGO/Virgo Stochastic Background group for helpful comments and discussions. The authors would also like to thank the scientists on site at the Virgo and LIGO detectors for installation and maintenance of the low-noise magnetometers whose data we used in this paper. Parts of this research were conducted by the Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav), through Project No. CE170100004. K. M. is supported by King's College London through a Postgraduate International Scholarship. N. C. acknowledges support from National Science Foundation Grant No. PHY-1806990. M. S. is supported in part by the Science and Technology Facility Council (STFC), United Kingdom, under Research Grant No. ST/P000258/1. This paper has been given LIGO DCC number P2000258. Numerous software packages were used in this paper. These include MATPLOTLIB [42], NumPy [43], SciPy [44], BILBY [40], CPNest [39], ChainConsumer [45], and SEABORN [46].