On measuring the gravitational-wave background using Pulsar Timing Arrays
Rutger van Haasteren, Yuri Levin, Patrick McDonald, Tingting Lu

TL;DR
This paper introduces a Bayesian algorithm for detecting the gravitational-wave background using Pulsar Timing Arrays, capable of analyzing data without information loss and handling various noise sources, improving detection strategies.
Contribution
The paper presents a novel Bayesian method that simultaneously measures GWB amplitude and slope, handles uneven data and noise, and outperforms existing approaches in PTA data analysis.
Findings
The algorithm effectively measures GWB parameters in simulated data.
Red noise in pulsar timing significantly impacts GWB detection.
Optimal observing strategies depend on experiment duration, number of pulsars, and noise levels.
Abstract
Long-term precise timing of Galactic millisecond pulsars holds great promise for measuring the long-period (months-to-years) astrophysical gravitational waves. Several gravitational-wave observational programs, called Pulsar Timing Arrays (PTA), are being pursued around the world. Here we develop a Bayesian algorithm for measuring the stochastic gravitational-wave background (GWB) from the PTA data. Our algorithm has several strengths: (1) It analyses the data without any loss of information, (2) It trivially removes systematic errors of known functional form, including quadratic pulsar spin-down, annual modulations and jumps due to a change of equipment, (3) It measures simultaneously both the amplitude and the slope of the GWB spectrum, (4) It can deal with unevenly sampled data and coloured pulsar noise spectra. We sample the likelihood function using Markov Chain Monte Carlo (MCMC)…
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