Speaker
Description
Gravitational wave observations of binary neutron star mergers ahve the potential to revolutionize our understanding of the nuclear equation of state and the fundamental interactions that determine its properties. A major hurdle in obtaining this nuclear information comes from the computational cost to solve the neutron star structure equations (Tolman-Oppenheimer-Volkoff equations) alongside calculation of the equation of state. In this talk, I will discuss our approach at removing this hurdle by using a variety of machine-learning techniques which greatly simplify and, therefore, speed-up the calculations necessary for on-the-fly calculations. In doing so, we first construct emulators for the solutions of the neutron star structure equations for several high-fidelity calculations. Then we implement these emulators into the PyCBC gravitational wave inference package to obtain posteriors on the nuclear parameters utilized in our equation of state model. These posteriors are directly sampled and therefore allow for direct parameter estimation of the nuclear equation of state from gravitational waves. Future prospects and outlooks from the creation of next-generation detectors will be discussed.
