Noise-marginalized optimal statistic: A robust hybrid frequentist-Bayesian statistic for the stochastic gravitational-wave background in pulsar timing arrays
Abstract
Observations have revealed that nearly all galaxies contain supermassive black holes (SMBHs) at their centers. When galaxies merge, these SMBHs form SMBH binaries (SMBHBs) that emit low-frequency gravitational waves (GWs). The incoherent superposition of these sources produce a stochastic GW background (GWB) that can be observed by pulsar timing arrays. The optimal statistic is a frequentist estimator of the amplitude of the GWB that specifically looks for the spatial correlations between pulsars induced by the GWB. In this paper, we introduce an improved method for computing the optimal statistic that marginalizes over the red noise in individual pulsars. We use simulations to demonstrate that this method more accurately determines the strength of the GWB, and we use the noise-marginalized optimal statistic to compare the significance of monopole, dipole, and Hellings-Downs (HD) spatial correlations and perform sky scrambles.
Additional Information
© 2018 American Physical Society. Received 1 June 2018; published 1 August 2018. We thank Joe Lazio, Andrea Lommen, Joe Romano, Xavier Siemens, and Jolien Creighton for useful discussions. This work was supported by NSF Physics Frontier Center Grant No. 1430284. J. A. E. was partially supported by NASA through Einstein Fellowship Grant No. PF4-150120. We are grateful for computational resources provided by the Leonard E. Parker Center for Gravitation, Cosmology and Astrophysics at the University of Wisconsin–Milwaukee, which is supported by NSF Grants No. 0923409 and No. 1626190.Attached Files
Published - PhysRevD.98.044003.pdf
Submitted - 1805.12188.pdf
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Additional details
- Eprint ID
- 88427
- Resolver ID
- CaltechAUTHORS:20180801-091128880
- NSF
- PHY-1430284
- NASA Einstein Fellowship
- PF4-150120
- NSF
- PHY-0923409
- NSF
- PHY-1626190
- Created
-
2018-08-01Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field
- Caltech groups
- TAPIR