  A sizeable fraction of gamma-ray burst (GRB) time profiles consist of a temporal sequence of pulses. The nature of this stochastic process carries information on how GRB inner engines work. The so-called interpulse time defines the interval between adjacent pulses, excluding the long quiescence periods during which the signal drops to the background level. It was found by many authors in the past that interpulse times are lognormally distributed, at variance with the exponential case that is expected for a memoryless process. We investigated whether the simple hypothesis of a temporally uncorrelated sequence of pulses is really to be rejected, as a lognormal distribution necessarily implies. We selected and analysed a number of multi--peaked CGRO/BATSE GRBs and simulated similar time profiles, with the crucial difference that we assumed exponentially distributed interpulse times, as is expected for a memoryless stationary Poisson process. We then identified peaks in both data sets using a novel peak search algorithm, which is more efficient than others used in the past. We independently confirmed that the observed interpulse time distribution is approximately lognormal. However, we found the same results on the simulated profiles, in spite of the intrinsic exponential distribution. Although intrinsic lognormality cannot be ruled out, this shows that intrinsic interpulse time distribution in real data could still be exponential, while the observed lognormal could be ascribed to the low efficiency of peak search algorithms at short values combined with the limitations of a bin-integrated profile. Our result suggests that GRB engines may emit pulses after the fashion of nuclear radioactive decay, that is, as a memoryless process. 
Gamma-ray burst engines may have no memory