Title: Exploring the Capabilities of Pulsar Timing Arrays as a Viable Probe of Supermassive Black Hole Binary Population using Machine Learning
Abstract: Recent pulsar timing array (PTA) experiments have found evidence for a nano-hertz gravitational wave background (GWB), prompting extensive investigation into its possible origins. While many studies have performed parameter estimation for proposed sources, few assess the statistical viability of their models for inference from PTA data. A complete analysis of the source of the GWB requires understanding the degeneracy and saturation points of its model parameters, where additional data no longer improves constraints, as well as the extent of prior dominance of the parameters in the Bayesian analyses. In this talk, I highlight my study on the statistical viability of a widely used semi-analytic population model of supermassive black hole binaries using simple machine learning techniques. The study has substantial implications for the limitations of astrophysical modeling and inferences, framing the expectations for the near and far future of PTAs.
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