Gamma-ray burst afterglows: constraining physical parameters and fireball model assumptions

Abstract

Gamma-ray bursts (GRBs) are the most luminous events in the high-energy sky. Occurring at cosmological distances, they are cataclysmic events presumed to be associated with the endpoints of massive stars' lives. They are produced by relativistic ejecta. As the ejecta encounters the surrounding medium it slows and emits lower-energy emission known as the GRB afterglow. The evolution of the shock in this phase depends upon the medium encountered as well as on such basic parameters of the event as energy and collimation. Afterglow studies can shed light upon the physics of relativistic shocks and the GRB environment(s), providing indirect clues to the progenitors. These parameters can be determined by fitting afterglow data sets to a model of the event, here, the fireball model. If the model assumptions are correct, the parameters providing a good fit will correspond to those of the event. We develop a fireball model starting from its analytic, asymptotic behaviour parameterized by its fundamental parameters (energy, collimation, density, and microphysics). We find good fits to four of the best-sampled broadband afterglow data sets, with simple assumptions concerning the unknown microphysics and circumburst density profile. We present the resulting fit parameters, showing reasonable energies, densities similar to those of diffuse clouds, and a large spread in such microphysical parameters as the fraction of shock energy used to generate magnetic fields. We also present results where the model fit showed degeneracies and other data sets that are not well-fit by this model. Motivated to determine the model's inherent uncertainty from the adoption of physical assumption, we consider some changes to these. We present our results: that a range of magnetic energy fraction variation with shock strength is permissible, and that afterglow fits are not sensitive to steeply rising circumburst power law density profiles. We demonstrate that the fitted parameters change when the assumptions are changed; this may be by a small fraction, or up to an order of magnitude.