2024A&A...689A.266B

Context. The complexity and variety exhibited by the light curves of long gamma-ray bursts (GRBs) enclose a wealth of information that has not yet been fully deciphered. Despite the tremendous advance in the knowledge of the energetics, structure, and composition of the relativistic jet that results from the core collapse of the progenitor star, the nature of the inner engine, how it powers the relativistic outflow, and the dissipation mechanisms remain open issues.
Aims. A promising way to gain insights is describing GRB light curves as the result of a common stochastic process. In the Burst And Transient Source Experiment (BATSE) era, a stochastic pulse avalanche model was proposed and tested through the comparison of ensemble-average properties of simulated and real light curves. Here our aim was to revive and further test this model.
Methods. We applied it to two independent datasets, BATSE and Swift/BAT, through a machine learning approach: the model parameters are optimised using a genetic algorithm.
Results. The average properties were successfully reproduced. Notwithstanding the different populations and passbands of both datasets, the corresponding optimal parameters are interestingly similar. In particular, for both sets the dynamics appear to be close to a critical state, which is key to reproducing the observed variety of time profiles.
Conclusions. Our results propel the avalanche character in a critical regime as a key trait of the energy release in GRB engines, which underpins some kind of instability.