  The variety of long duration gamma-ray burst (LGRB) light curves (LCs) encode
a wealth of information on how LGRB engines release energy following the
collapse of the progenitor star. Attempts to characterise GRB LCs focused on a
number of properties, such as the minimum variability timescale, power density
spectra (both ensemble average and individual), or with different definitions
of variability. In parallel, a characterisation as a stochastic process was
pursued by studying the distributions of waiting times, peak flux, fluence of
individual peaks within GRB time profiles. Yet, the question remains as to
whether the diversity of profiles can be described in terms of a common
stochastic process. Here we address this issue by studying for the first time
the distribution of the number of peaks in a GRB profile. We used four
different GRB catalogues: CGRO/BATSE, Swift/BAT, BeppoSAX/GRBM, and
Insight-HXMT. The statistically significant peaks were identified by means of
well tested algorithm MEPSA and further selected by applying a set of
thresholds on signal-to-noise ratio. We then extracted the corresponding
distributions of number of peaks per GRB. Among the different models considered
(power-law, simple or stretched exponential) only a mixture of two exponentials
models all the observed distributions, suggesting the existence of two distinct
behaviours: (i) an average number of 2.1+-0.1 peaks per GRB ("peak poor") and
accounting for about 80% of the observed population of GRBs; (ii) an average
number of 8.3+-1.0 peaks per GRB ("peak rich") and accounting for the remaining
20% of the observed population. We associate the class of peak-rich GRBs with
the presence of sub-second variability, which seems to be absent among
peak-poor GRBs. The two classes could result from two different regimes through
which GRB engines release energy or through which energy is dissipated into
gamma-rays.
