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

• Published in: arXiv.org 2024-02
• Main Authors: Guidorzi, C, Sartori, M, Maccary, R, Tsvetkova, A, Amati, L, Bazzanini, L, Bulla, M, Camisasca, A E, Ferro, L, Frontera, F, Li, C K, Xiong, S L, Zhang, S N
• Format: Article
• Language: English
• Subjects: Algorithms; Engines; Fluence; Gamma ray bursts; Gamma rays; Light curve; Signal to noise ratio; Stochastic models; Stochastic processes
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2402.17282

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.