A normalizing flow emulator trained on the full ensemble distribution of gravitational wave strain from simulated supermassive black hole binaries matches the simulations more closely than the Gaussian process emulator used in prior NANOGrav analyses.
Exploring the Capabilities of Gibbs Sampling in Pulsar Timing Arrays
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We explore the use of Gibbs sampling in estimating the noise properties of individual pulsars and illustrate its effectiveness using the NANOGrav 11-year data set. We find that Gibbs sampling noise modeling (GM) is more efficient than the current standard Bayesian techniques (SM) for single pulsar analyses by yielding model parameter posteriors with average effective-sample-size ratio (GM/SM) of 6 across all parameters and pulsars. Furthermore, the output of GM contains posteriors for the Fourier coefficients that can be used to characterize the underlying red noise process of any pulsar's timing residuals, which are absent in current implementations of SM. Through simulations, we demonstrate the potential for such coefficients to measure the spatial cross-correlations between pulsar pairs produced by a gravitational wave background.
fields
astro-ph.IM 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Deep Neural Emulation of the Supermassive Black-hole Binary Population
A normalizing flow emulator trained on the full ensemble distribution of gravitational wave strain from simulated supermassive black hole binaries matches the simulations more closely than the Gaussian process emulator used in prior NANOGrav analyses.