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.
16 vanHaasteren, R., 2024b
3 Pith papers cite this work, alongside 15 external citations. Polarity classification is still indexing.
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Customized chromatic noise models for 67 pulsars detect non-dispersive delays in 21 cases, alter achromatic noise inferences in 19, and enable solar wind density estimates over 1.5 cycles.
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.
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The NANOGrav 15 yr Data Set: Customized Chromatic Noise Models
Customized chromatic noise models for 67 pulsars detect non-dispersive delays in 21 cases, alter achromatic noise inferences in 19, and enable solar wind density estimates over 1.5 cycles.
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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.