Speaker
Description
In summer 2023, multiple PTA collaborations reported evidence for a gravitational wave background in the nanohertz regime. Searching for anisotropies is considered a promising way to discriminate between an astrophysical and a cosmological signal origin, but existing methods face key limitations: Bayesian approaches are computationally expensive, while frequentist methods rely on a Gaussian likelihood despite strongly non-Gaussian statistics of the pulsar pair correlations.
We present a simulation-based, likelihood-free inference framework that replaces the analytic likelihood with a neural network classifier trained on synthetic data. This approach captures the full non-Gaussian structure of the pair correlation estimators and significantly improves performance, more than doubling detection probabilities compared to standard frequentist methods.