By injecting eccentric binary signals into simulated PTA data, the paper shows individual masses are recoverable at high frequencies, while low-frequency signals can be confused with the stochastic background and yield Bayes factors near unity.
Noise analysis in the European Pulsar Timing Array data release 2 and its implications on the gravitational-wave background search
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abstract
The European Pulsar Timing Array (EPTA) collaboration has recently released an extended data set for six pulsars (DR2) and reported evidence for a common red noise signal. Here we present a noise analysis for each of the six pulsars. We consider several types of noise: (i) radio frequency independent, "achromatic", and time-correlated red noise; (ii) variations of dispersion measure and scattering; (iii) system and band noise; and (iv) deterministic signals (other than gravitational waves) that could be present in the PTA data. We perform Bayesian model selection to find the optimal combination of noise components for each pulsar. Using these custom models we revisit the presence of the common uncorrelated red noise signal previously reported in the EPTA DR2 and show that the data still supports it with a high statistical significance. Next, we confirm that there is no preference for or against the Hellings-Downs spatial correlations expected for the stochastic gravitational-wave background. The main conclusion of the EPTA DR2 paper remains unchanged despite a very significant change in the noise model of each pulsar. However, modelling the noise is essential for the robust detection of gravitational waves and its impact could be significant when analysing the next EPTA data release, which will include a larger number of pulsars and more precise measurements.
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Parameter Estimation for Eccentric Supermassive Black Hole Binaries with Pulsar Timing Arrays
By injecting eccentric binary signals into simulated PTA data, the paper shows individual masses are recoverable at high frequencies, while low-frequency signals can be confused with the stochastic background and yield Bayes factors near unity.