Rapid gravitational wave source properties inference using supervised learning

Compact binary coalescences of at least one neutron star component is of special interest in the field of transient astronomy. These objects are strong emitters of well-modeled gravitational waves and simultaneously progenitors of short GRBs and kilonova. Rapid identification of the properties of these events that can inform us about the probability of having an electromagnetic counterpart (EM-Bright) is of great importance to the transient astronomy community. In this presentation I will talk about the latest updates in this work. I will show how we use supervised learning to improve the estimates of the EM-Bright probabilities in realtime. I will also present how we incorporate information from earlier events (for example inferences on the equation of state) to calculate more astrophysically accurate probabilities of EM-Bright.