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  Solving the PTA Data Analysis Problem with a Global Gibbs Scheme

Laal, N., Taylor, S. R., van Haasteren, R., Lamb, W. G., & Siemens, X. (submitted). Solving the PTA Data Analysis Problem with a Global Gibbs Scheme.

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 Creators:
Laal, Nima, Author
Taylor, Stephen R., Author
van Haasteren, Rutger1, Author           
Lamb, William G, Author
Siemens, Xavier, Author
Affiliations:
1Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, ou_24011              

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Free keywords: Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM,General Relativity and Quantum Cosmology, gr-qc
 Abstract: The announcement in the summer of 2023 about the discovery of evidence for a
gravitational wave background (GWB) using pulsar timing arrays (PTAs) has
ignited both the PTA and the larger scientific community's interest in the
experiment and the scientific implications of its findings. As a result,
numerous scientific works have been published analyzing and further developing
various aspects of the experiment, from performing tests of gravity to
improving the efficiency of the current data analysis techniques. In this
regard, we contribute to the recent advancements in the field of PTAs by
presenting the most general, agnostic, per-frequency Bayesian search for a
low-frequency (red) noise process in these data. Our new method involves the
use of a conjugate Jeffrey's-like multivariate prior which allows one to model
all unique parameters of the global PTA-level red noise covariance matrix as a
separate model parameter for which a marginalized posterior-probability
distribution can be found using Gibbs sampling. Even though perfecting the
implementation of the Gibbs sampling and mitigating the numerical stability
challenges require further development, we show the power of this new method by
analyzing realistic and theoretical PTA simulated data sets. We show how our
technique is consistent with the more restricted standard techniques in
recovering both the auto and the cross-spectrum of pulsars' low-frequency (red)
noise. Furthermore, we highlight ways to approximately characterize a GWB (both
its auto- and cross-spectrum) using Fourier coefficient estimates from
single-pulsar and so-called CURN (common uncorrelated red noise) analyses via
analytic draws from a specific Inverse-Wishart distribution.

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 Dates: 2024-10-152024
 Publication Status: Submitted
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 2410.11944
 Degree: -

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