A one-parameter massive-gravity correlation curve gives lower chi-square than the Hellings-Downs curve for current pulsar-timing data, but the parameter is fitted to the data, so the result is not a prediction.
Comparative analysis of the NANOgrav Hellings-Downs as a window into new physics
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abstract
Pulsar timing array (PTA) experiments have recently provided strong evidence for the signal of the stochastic gravitational wave background (SGWB) in the nHz-frequency band. These experiments have shown a statistical preference for the Hellings-Downs (HD) correlation between pulsars, which is widely regarded as a definitive signature of the SGWB. Using the NANOGrav 15-year dataset, we perform a comparative Bayesian analysis of four different models that go beyond the standard cosmological framework and influence the overlap reduction function. Specifically, we analyze ultralight vector dark matter (DM), spin-2 ultralight DM, massive gravity, and a folded non-Gaussian component to the SGWB. We find that the spin-2 ultralight DM and the massive gravity model are statistically equivalent to the HD prediction, and there is weak evidence in favor of the non-Gaussian component and the ultralight vector DM model. We also perform a non-parametric test using the Genetic Algorithms, which suggests a weak deviation from the HD curve. However, improved data quality is required before drawing definitive conclusions.
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Do Pulsar Timing Datasets Favor Massive Gravity?
A one-parameter massive-gravity correlation curve gives lower chi-square than the Hellings-Downs curve for current pulsar-timing data, but the parameter is fitted to the data, so the result is not a prediction.