An advanced pulse-avalanche stochastic model of long gamma-ray burst light curves
A unified explanation of the variety of long-duration gamma-ray burst (GRB) light curves (LCs) is essential for identifying the dissipation mechanism and possibly the nature of their central engines. In the past, a model was proposed to describe GRB LCs as the outcome of a stochastic pulse avalanche process, possibly originating from a turbulent regime, and it was tested by comparing average temporal properties of simulated and real LCs. Recently, we revived this model and optimised its parameters using a genetic algorithm (GA), a machine-learning-based approach. Our findings suggested that GRB inner engines may operate near a critical regime. Here we present an advanced version of the model, which allows us to constrain the peak flux distribution of individual pulses, and evaluate its performance on a new dataset of GRBs observed by the Fermi Gamma-ray Burst Monitor (GBM). After introducing new model parameters and a further comparison metric, that is the observed signal-to-noise (S/N) distribution, we test the new model on three complementary datasets: CGRO/BATSE, Swift/BAT, and Fermi/GBM. As in our previous work, the model parameters are optimised using a GA. The updated sets of parameters achieve a further reduction in loss compared to both the original model and our earlier optimisation. The different values of the parameters across the datasets are shown to originate from the different energy passbands, effective areas, trigger algorithms, and, ultimately, different GRB populations of the three experiments. Our results further underpin the stochastic and avalanche character of the dissipation process behind long GRB prompt emission, with an emphasis on the near-critical behaviour, and establish this new model as a reliable tool for generating realistic GRB LCs as they would be seen with future experiments.