Fast flavor oscillation in neutron star mergers

Neutrino flavor oscillation in neutron star mergers will significantly affect the dynamics of the neutron star mergers and the electron fraction for nucleosynthesis calculations. In particular, fast flavor instability close to the central object in neutron star merger simulations can increase the production of r-process elements. However, the length and timescale of such instabilities make it challenging and computationally expensive to simulate on the fly. Using a physics-informed machine learning model, we can predict the outcome of fast flavor instability. In this talk, I will discuss our approach to include fast flavor instability calculations in neutron star merger simulations. I will also present our current machine-learning model and show how well we can predict fast flavor instability.