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.
Noise-marginalized optimal statistic: A robust hybrid frequentist-Bayesian statistic for the stochastic gravitational-wave background in pulsar timing arrays
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
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 (PTAs). 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.
fields
astro-ph.CO 3representative citing papers
Combined five-PTA dataset yields posterior on SGWB power-law amplitude and index consistent with nonzero signal but below 5-sigma significance, with reconstructed angular correlations matching the Hellings-Downs prediction.
Customized chromatic noise models applied to NANOGrav 15 yr data raise the Bayes factor for Hellings-Downs GWB correlations by a factor of ~8, lower the amplitude to 2.1e-15, and increase the spectral index to 3.5.
citing papers explorer
-
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.
-
Stochastic gravitational-wave background search using data from five pulsar timing arrays
Combined five-PTA dataset yields posterior on SGWB power-law amplitude and index consistent with nonzero signal but below 5-sigma significance, with reconstructed angular correlations matching the Hellings-Downs prediction.
-
The NANOGrav 15 yr Data Set: Impacts of Customized Chromatic Noise Models on Gravitational Wave Analyses
Customized chromatic noise models applied to NANOGrav 15 yr data raise the Bayes factor for Hellings-Downs GWB correlations by a factor of ~8, lower the amplitude to 2.1e-15, and increase the spectral index to 3.5.