  On the 17th of August, 2017 came the simultaneous detections of GW170817, a
gravitational wave that originated from the coalescence of two neutron stars,
along with the gamma-ray burst GRB170817A, and the kilonova counterpart
AT2017gfo. Since then, there has been much excitement surrounding the study of
neutron star mergers, both observationally, using a variety of tools, and
theoretically, with the development of complex models describing the
gravitational-wave and electromagnetic signals. In this work, we improve upon
our pipeline to infer kilonova properties from observed light-curves by
employing a Neural-Network framework that reduces execution time and handles
much larger simulation sets than previously possible. In particular, we use the
radiative transfer code POSSIS to construct 5-dimensional kilonova grids where
we employ different functional forms for the angular dependence of the
dynamical ejecta component. We find that incorporating an angular dependence
improves the fit to the AT2017gfo light-curves by up to ~50% when quantified in
terms of the weighted Mean Square Error.
