  Gamma-ray bursts (GRBs) and double neutron-star merger gravitational wave
events are followed by afterglows that shine from X-rays to radio, and these
broadband transients are generally interpreted using analytical models. Such
models are relatively fast to execute, and thus easily allow estimates of the
energy and geometry parameters of the blast wave, through many trial-and-error
model calculations. One problem, however, is that such analytical models do not
capture the underlying physical processes as well as more realistic
relativistic numerical hydrodynamic (RHD) simulations do. Ideally, those
simulations are used for parameter estimation instead, but their computational
cost makes this intractable. To this end, we present DeepGlow, a highly
efficient neural network architecture trained to emulate a computationally
costly RHD-based model of GRB afterglows, to within a few percent accuracy. As
a first scientific application, we compare both the emulator and a different
analytical model calibrated to RHD simulations, to estimate the parameters of a
broadband GRB afterglow. We find consistent results between these two models,
and also give further evidence for a stellar wind progenitor environment around
this GRB source. DeepGlow fuses simulations that are otherwise too complex to
execute over all parameters, to real broadband data of current and future GRB
afterglows.
