Methods to Reduce Pulsar Timing Array Datasets for Stochastic Gravitational Wave Background Detection.

Pulsar timing array (PTA) datasets used for gravitational wave (GW) detection are complicated and can take years to fully understand, model, and analyze. In this presentation we will show a method to reduce the complexity of a PTA dataset by performing a frequency domain version of the optimal statistic used for stochastic background searches that only needs estimates of the pulsar’s power spectral densities and their red noise Fourier coefficients. The advantage of performing this version of the optimal statistic is that it is computationally less expensive, and one does not need to know the timing models or the noise models of the pulsars. This version of the optimal statistic returns up to 90% of the signal-to-noise ratio that the standard time domain optimal statistic yields on simulated datasets. The goal of this work is to produce reduced PTA datasets such as the NANOGrav 15-year dataset that we can give to students and non-PTA scientists that is easier to understand and analyze. This work could also serve to simplify future PTA analyses by reducing the components needed for the analysis.