The variety and complexity of long duration gamma–ray burst (LGRB) light curves (LCs) encode a wealth of information on the way LGRB engines release their energy following the collapse of the progenitor massive star. Thus far, 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 through 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 that can be identified within GRB time profiles. Yet, the question remains as to whether the diversity of GRB profiles can be described in terms of a common stochastic process. Here we address this issue by extracting and modelling for the first time the distribution of the number of peaks within a GRB profile. 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 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 turned out to model 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 instead appears to be absent among peak-poor GRBs. The two classes could result from two different regimes through which GRB inner engines release energy or through which energy is dissipated into gamma-rays.

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

Guidorzi, C.
;
Sartori, M.;Maccary, R.;Tsvetkova, A.;Amati, L.;Bazzanini, L.;Bulla, M.;Camisasca, A. E.;Ferro, L.;Frontera, F.;Zhang, S. N.
2024

Abstract

The variety and complexity of long duration gamma–ray burst (LGRB) light curves (LCs) encode a wealth of information on the way LGRB engines release their energy following the collapse of the progenitor massive star. Thus far, 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 through 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 that can be identified within GRB time profiles. Yet, the question remains as to whether the diversity of GRB profiles can be described in terms of a common stochastic process. Here we address this issue by extracting and modelling for the first time the distribution of the number of peaks within a GRB profile. 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 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 turned out to model 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 instead appears to be absent among peak-poor GRBs. The two classes could result from two different regimes through which GRB inner engines release energy or through which energy is dissipated into gamma-rays.
2024
Guidorzi, C.; Sartori, M.; Maccary, R.; Tsvetkova, A.; Amati, L.; Bazzanini, L.; Bulla, M.; Camisasca, A. E.; Ferro, L.; Frontera, F.; Li, C. K.; Xion...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2554810
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