A reparametrized hierarchical Bayesian approach using normalizing flows and orthogonal projection of hyperparameters yields tighter noise constraints and partially breaks the red-noise-SGWB degeneracy in a minimal 3-pulsar PTA analysis.
Monthly Notices of the Royal Astronomical Society 537, 3470–3479
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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.
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Addressing prior dependence in hierarchical Bayesian modeling for PTA data analysis II: Noise and SGWB inference through parameter decorrelation
A reparametrized hierarchical Bayesian approach using normalizing flows and orthogonal projection of hyperparameters yields tighter noise constraints and partially breaks the red-noise-SGWB degeneracy in a minimal 3-pulsar PTA analysis.