The jet structure plays an important role in both the prompt and afterglow emission phases of gamma-ray bursts (GRBs). Whether GRB jets are better described by uniform (top-hat) or structured models remains an open question. We use the afterglowpy Python package to numerically model the late X-ray afterglow light curves of a large sample of long and short GRBs, and apply the Bayesian Information Criterion (BIC) to compare the performance of top-hat and Gaussian structured jet models. Within our adopted modeling framework, we find that the top-hat model is preferred by the BIC for ∼78.9% (150/190) of long GRBs and 70% (7/10) of short GRBs. GRB 180205A and GRB 140515A exhibit ΔBIC < 2 for all three model comparisons, indicating that top-hat, Gaussian, and power-law jets provide equivalent fits to their afterglow light curves. This large-sample analysis suggests that uniform jets may be more common than structured jets in the observed GRB population, although this conclusion is subject to the limitations of our model assumptions and the BIC-based model selection criterion. Furthermore, we find that the best-fit distributions of observer angle θobs, electron energy fraction ϵe, and isotropic equivalent energy E0 differ significantly between the top-hat and Gaussian jet models, with θobs showing the most pronounced distinction.
Liao et al. (Fri,) studied this question.