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How to Detect an Astrophysical Nanohertz Gravitational Wave Background
Journal article   Open access   Peer reviewed

How to Detect an Astrophysical Nanohertz Gravitational Wave Background

Bence Bécsy, Neil Cornish, Patrick Meyers, Luke Kelley, Gabriella Agazie, Akash Anumarlapudi, Anne Archibald, Zaven Arzoumanian, Paul Baker, Laura Blecha, …
The Astrophysical journal, Vol.959(1), p.9
01/12/2023
DOI: https://doi.org/10.3847/1538-4357/ad09e4

Abstract

Astronomical models Black holes Datasets Frequencies Gravitational waves Isotropy Noise levels Parameters Pulsars Simulation Statistical methods Supermassive black holes
Analyses of pulsar timing data have provided evidence for a stochastic gravitational wave background in the nanohertz frequency band. The most plausible source of this background is the superposition of signals from millions of supermassive black hole binaries. The standard statistical techniques used to search for this background and assess its significance make several simplifying assumptions, namely (i) Gaussianity, (ii) isotropy, and most often, (iii) a power-law spectrum. However, a stochastic background from a finite collection of binaries does not exactly satisfy any of these assumptions. To understand the effect of these assumptions, we test standard analysis techniques on a large collection of realistic simulated data sets. The data-set length, observing schedule, and noise levels were chosen to emulate the NANOGrav 15 yr data set. Simulated signals from millions of binaries drawn from models based on the Illustris cosmological hydrodynamical simulation were added to the data. We find that the standard statistical methods perform remarkably well on these simulated data sets, even though their fundamental assumptions are not strictly met. They are able to achieve a confident detection of the background. However, even for a fixed set of astrophysical parameters, different realizations of the universe result in a large variance in the significance and recovered parameters of the background. We also find that the presence of loud individual binaries can bias the spectral recovery of the background if we do not account for them.
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