Distribution of the number of peaks within a long gamma-ray burst

¹ Department of Physics and Earth Science, University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy
² INFN – Sezione di Ferrara, Via Saragat 1, 44122 Ferrara, Italy
e-mail: guidorzi@infn.it
³ INAF – Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, Via Piero Gobetti 101, 40129 Bologna, Italy
⁴ Department of Physics, University of Cagliari, SP Monserrato-Sestu, km 0.7, 09042 Monserrato, Italy
⁵ Ioffe Institute, Politekhnicheskaya 26, 194021 St. Petersburg, Russia
⁶ INAF – Osservatorio Astronomico d’Abruzzo, Via Mentore Maggini snc, 64100 Teramo, Italy
⁷ Key Laboratory of Particle Astrophysics, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, PR China
⁸ University of Chinese Academy of Sciences, Beijing 100049, PR China

Received: 10 January 2024
Accepted: 23 February 2024

Abstract

Context. The variety and complexity of long duration gamma-ray burst (LGRB) light curves (LCs) encode a wealth of information about the way LGRB engines release their energy following the collapse of the progenitor massive star. Thus far, attempts to characterise GRB LCs have focused on a number of properties, such as the minimum variability timescale and power density spectra (both ensemble average and individual), or considering different definitions of variability. In parallel, a characterisation as a stochastic process has been pursued by studying the distributions of waiting times, peak flux, and fluence of individual peaks that can be identified within GRB time profiles. However, an important question remains as to whether the diversity of GRB profiles can be described in terms of a common stochastic process.

Aims. Here, we address this issue by extracting and modelling, for the first time, the distribution of the number of peaks within a GRB profile.

Methods. We analysed four different GRB catalogues: CGRO/BATSE, Swift/BAT, BeppoSAX/GRBM, and Insight-HXMT. The statistically significant peaks were identified by means of well tested and calibrated algorithm MEPSA and further selected by applying a set of thresholds on the signal-to-noise ratio. We then extracted the corresponding distributions of number of peaks per GRB.

Results. Among the different models considered (power-law, simple or stretched exponential), we find that only a mixture of two exponentials was able to model all the observed distributions. This suggests the existence of two distinct behaviours: (i) an average number of 2.1 ± 0.1 peaks per GRB (“peak-poor”), accounting for about 80% of the observed population of GRBs; and (ii) an average number of 8.3 ± 1.0 peaks per GRB (“peak-rich”), accounting for the remaining 20% of the observed population.

Conclusions. We associate the class of peak-rich GRBs with the presence of sub-second variability, which appears to be surprisingly absent among peak-poor GRBs. The two classes could result from two distinct regimes in which the inner engines of GRBs release their energy or otherwise dissipate that energy as gamma rays.