  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.
