Simulation-Based Inference For Pulsar Timing Arrays

Recently, pulsar timing arrays have discovered evidence of a gravitational wave background. Understanding the origins of this signal and extracting meaningful astrophysics from it requires comparing the signal with a wide array of models ranging from supermassive black hole binaries to more exotic sources. Therefore, it is becoming increasingly imperative to develop an efficient pipeline for comparing model outputs to pulsar timing data. Traditionally this work has been done with Gaussian process interpolation and Monte Carlo Markov Chain algorithms, however machine learning techniques, like simulation-based inference (SBI), provide a new approach. In this talk, I will discuss how SBI can help bridge the gap between models and data while allowing for efficient exploration of complicated astrophysical parameter spaces.