Julian Chan

Accurately modellng the merger timescales of supermassive binary black holes (BHBs) is crucial for interpreting the stochastic gravitational wave background (GWB) detected by Pulsar Timing Arrays. To address this challenge, neural ordinary differential equations (Neural ODEs) are employed to distill the coarse-grained dynamics of binary black holes, yielding compact and interpretable models of their evolutionary trajectories. Complementarily, graph-based learning approaches, particularly spatio-temporal graph neural networks, are used to model the full phase space of N-body systems relevant to galaxy mergers involving binary black holes, capturing detailed and time-varying interactions. By integrating these state-of-the-art machine learning techniques, this research enhances predictions of BHB merger timescales and furthers astrophysical insights into the origins and properties of the GWB as observed by Pulsar Timing Arrays.