The discovery of GW170817 provided the first empirical evidence that merging binary neutron star systems are both progenitors of short gamma-ray bursts, as well as the primary sites of the nucleosynthetic rapid-neutron capture process. Initially detected as gravitational wave (GW) and gamma-ray burst (GRB) triggers, GW170817 was well-localized and follow-up observations detected a kilonova along with the GRB afterglow. Gamma-ray burst afterglows encode information about jet geometry and the environments in which the relativistic jets propagated. However, the information we can garner is limited to regions that are transparent to radiation—meaning it cannot directly give us information about the central engine, the properties of the jet at injection, or tell us how that jet acquires its structure. By using Bayesian parameter estimation analysis to connect astrophysical data with numerical models, we can begin to constrain the properties of these unobservable regions and improve our understandings of short GRBs and neutron star matter. In this work, we performed two separate analyses using emcee, a Python implementation of the Markov Chain Monte Carlo
sampling method. We first constrained jet, environment, and observer parameters of a GRB afterglow resulting from an off-axis structured jet. We then used a subset of those results as data in our second analysis to constrain properties of the central engine, neutron star ejecta, and the jet at injection using an outflow model that simulates baryon-loaded wind and jet interactions. In total we were able to constrain 17 parameters, the largest parameter space so far explored using structured jet outflow and afterglow models. We anticipate that with future joint GW-GRB observations of binary neutron star merger events, similar techniques can be used to further probe the nature of neutron star matter, and by extension a neutron star’s equation of state.