Journal article
Simultaneous estimation of astrophysical and cosmological stochastic gravitational-wave backgrounds with terrestrial detectors
K Martinovic, PM Meyers, M Sakellariadou, N Christensen
Physical Review D | AMER PHYSICAL SOC | Published : 2021
Abstract
The recent Advanced LIGO and Advanced Virgo joint observing runs have not claimed a stochastic gravitational-wave background detection, but one expects this to change as the sensitivity of the detectors improves. The challenge of claiming a true detection will be immediately succeeded by the difficulty of relating the signal to the sources that contribute to it. In this paper, we consider backgrounds that comprise compact binary coalescences and additional cosmological sources, and we set simultaneous upper limits on these backgrounds. We find that the Advanced LIGO/Advanced Virgo network, operating at design sensitivity, will not allow for separation of the sources we consider. Third-genera..
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Grants
Awarded by King’s College London
Funding Acknowledgements
The authors would like to thank the LIGO/Virgo Stochastic Background group for helpful comments and discussions, in particular Alexander Jenkins for his numerical simulations of cosmic string spectra, and Tania Regimbau for useful discussions. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants No. PHY-0757058 and PHY0823459. 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. M. S. is supported in part by the Science and Technology Facility Council (STFC), United Kingdom, under the research Grant No. ST/P000258/1. N. C. acknowledges support from National Science Foundation Grant No. PHY-1806990. This paper has been given LIGO DCC number P2000470. Numerous software packages were used in this paper. These include Matplotlib [73], NumPy [74], SciPy [75], BILBY [76], cpnest [77], ChainConsumer [78], and seaborn [79].