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Gamma-ray bursts (GRBs) and double neutron-star merger gravitational waveevents are followed by afterglows that shine from X-rays to radio, and thesebroadband transients are generally interpreted using analytical models. Suchmodels are relatively fast to execute, and thus easily allow estimates of theenergy and geometry parameters of the blast wave, through many trial-and-errormodel calculations. One problem, however, is that such analytical models do notcapture the underlying physical processes as well as more realisticrelativistic numerical hydrodynamic (RHD) simulations do. Ideally, thosesimulations are used for parameter estimation instead, but their computationalcost makes this intractable. To this end, we present DeepGlow, a highlyefficient neural network architecture trained to emulate a computationallycostly RHD-based model of GRB afterglows, to within a few percent accuracy. Asa first scientific application, we compare both the emulator and a differentanalytical model calibrated to RHD simulations, to estimate the parameters of abroadband GRB afterglow. We find consistent results between these two models,and also give further evidence for a stellar wind progenitor environment aroundthis GRB source. DeepGlow fuses simulations that are otherwise too complex toexecute over all parameters, to real broadband data of current and future GRBafterglows.

DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events