A hierarchical Bayesian inference framework combining free-spectrum reconstruction with population-level likelihoods distinguishes finite SMBHB populations from Gaussian primordial GWB using mock PTA data.
The impact of cosmic variance on PTAs anisotropy searches
4 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Several Pulsar Timing Array (PTA) collaborations have recently found evidence for a Gravitational Wave Background (GWB) by measuring the perturbations that this background induces in the time-of-arrivals of pulsar signals. These perturbations are expected to be correlated across different pulsars and, for isotropic GWBs, the expected values of these correlations (obtained by averaging over different GWB realizations) are a simple function of the pulsars' angular separations, known as the Hellings-Downs (HD) correlation function. On the other hand, anisotropic GWBs would induce deviations from these HD correlations in a way that can be used to search for anisotropic distributions of the GWB power. However, even for isotropic GWBs, interference between GW sources radiating at overlapping frequencies induces deviations from the HD correlation pattern, an effect known in the literature as "cosmic variance". In this work, we study the impact of cosmic variance on PTA anisotropy searches. We find that the fluctuations in cross-correlations related to cosmic variance can lead to the miss-classification of isotropic GWBs as anisotropic, leading to a false detection rate of ~50% for frequentist anisotropy searches. We also observe that cosmic variance complicates the reconstruction of the GWB sky map, making it more challenging to resolve bright GW hotspots, like the ones expected to be produced from a Supermassive Black Hole Binaries population. These findings highlight the need to refine anisotropy search techniques to improve our ability to reconstruct the GWB sky map and accurately assess the significance of any isotropy deviations we might find in it.
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A neural-network-based simulation inference method improves 3σ detection probability of gravitational-wave background anisotropies by 90-200% over Gaussian frequentist searches by learning non-Gaussian structure in pulsar timing residuals.
PTA statistical tests cannot distinguish Gaussian and non-Gaussian GWB amplitude distributions in a model-agnostic way after decorrelation.
Explores SKAO detection of scalar-induced GW backgrounds as probes of primordial non-Gaussianity and parity violation, with LSS cross-correlation to improve SNR.
citing papers explorer
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Population statistics of nanohertz gravitational wave sources
A hierarchical Bayesian inference framework combining free-spectrum reconstruction with population-level likelihoods distinguishes finite SMBHB populations from Gaussian primordial GWB using mock PTA data.
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Detecting Gravitational-Wave Anisotropies with Simulation-Based Inference
A neural-network-based simulation inference method improves 3σ detection probability of gravitational-wave background anisotropies by 90-200% over Gaussian frequentist searches by learning non-Gaussian structure in pulsar timing residuals.
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Are PTA measurements sensitive to gravitational wave non-Gaussianities?
PTA statistical tests cannot distinguish Gaussian and non-Gaussian GWB amplitude distributions in a model-agnostic way after decorrelation.
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Exploring Gravitational Wave Signatures Due to Primordial Non-gaussianity and Large Scale Structure Using SKAO
Explores SKAO detection of scalar-induced GW backgrounds as probes of primordial non-Gaussianity and parity violation, with LSS cross-correlation to improve SNR.