Analysis ofFermigamma-ray burst duration distribution

Abstract

Context. Two classes of gamma-ray bursts (GRBs), short and long, have been determined without any doubts, and are usually prescribed to different physical scenarios. A third class, intermediate in T 90 durations has been reported in the datasets of BATSE, Swift, RHESSI, and possibly BeppoSAX. The latest release of >1500 GRBs observed by Fermi gives an opportunity to further investigate the duration distribution. Aims. The aim of this paper is to investigate whether a third class is present in the log T 90 distribution, or whether it is described by a bimodal distribution. Methods. A standard χ 2 fitting of a mixture of Gaussians was applied to 25 histograms with different binnings. Results. Different binnings give various values of the fitting parameters, as well as the shape of the fitted curve. Among five statistically significant fits, none is trimodal. Conclusions. Locations of the Gaussian components are in agreement with previous works. However, a trimodal distribution, understood in the sense of having three distinct peaks, is not found for any binning. It is concluded that the duration distribution in the Fermi data is well described by a mixture of three log-normal distributions, but it is intrinsically bimodal, hence no third class is present in the T 90 data of Fermi. It is suggested that the log-normal fit may not be an adequate model.

Two dimensional classification of the Swift/BAT GRBs

“…3 connection between the third component and the XRFs might be true.Based on the above analysis in the two dimensional case, one can conclude that the Swift bursts with measured redshift are still better to be classified into two groups, which is in good agreement with some previous works (e.g. Zhang & Choi 2008;Qin & Chen 2013;Tarnopolski 2015;Yang et al 2016) The results of this work can be summarized as.…”

Distinguishing short and longFermigamma-ray bursts

“…It is important to note that in all mentioned research only a mixture of standard normal distributions (Gaussians) was fitted to the observed log T90 distributions. By examining the duration distribution it was shown that the third class is unlikely to be present in the Fermi data (Tarnopolski 2015a), and that a mixture of two intrinsically skewed distributions follows better the log T90 distribution of Fermi GRBs than a mixture of three standard Gaussians (Tarnopolski 2015b).…”

New results in applying the machine learning to GRB redshift estimation

“…It is generally accepted that there are short and long GRBs [2], the former is thought to be originating from binary neutron star mergers and the latter from hypernovas. Lately, with the help of multi-and uni-variate statistical analysis techniques an intermediate group was found [3,4,5,6,7,8,9,10,11,12,13]. Furthermore, recent publications have discovered large structures based on GRBs: the HerculesBorealis great wall at z ≈ 2 [14,15] and the Giant GRB ring at z ≈ 0.8 [16,17].…”