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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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.