Classification of Swift's gamma-ray bursts

Abstract

Context. Two classes of gamma-ray bursts have been identified in the BATSE catalogs characterized by durations shorter and longer than about 2 s. There are, however, some indications for the existence of a third class. Swift satellite detectors have different spectral sensitivity than pre-Swift ones for gamma-ray bursts. Therefore we reanalyze the durations and their distribution and also the classification of GRBs. Aims. We analyze the bursts duration distribution, published in The First BAT Catalog, whether it contains two, three or more groups. Methods. Using The First BAT Catalog the maximum likelihood estimation was used to analyze the duration distribution of GRBs.Results. The three log-normal fit is significantly (99.54% probability) better than the two for the duration distribution. Monte-Carlo simulations also confirm this probability (99.2%). Similarly, in previous results we found that the fourth component is not needed. The relative frequencies of the distribution of the groups are 7% short 35% intermediate and 58% long. Conclusions. Similarly to the BATSE data, three components are needed to explain the BAT GRBs' duration distribution. Although the relative frequencies of the groups are different than in the BATSE GRB sample, the difference in the instrument spectral sensitivities can explain this bias. This means theoretical models may be needed to explain three different type of gamma-ray bursts.

Classification of gamma-ray burst durations using robust model-comparison techniques

“…This was confirmed from further analysis of the complete BATSE dataset (Horváth 2002;Chattopadhyay et al 2007;Zitouni et al 2015). Evidence for a third lognormal component was also found in Swift/BAT data (Horváth et al 2008;Huja et al 2009;Horváth et al 2010) using duration and also from two-dimensional clustering using both duration and hardness (Veres et al 2010 Horváth & Tóth (2016), who pointed that three lognormal distributions provide a better fit to the data than two with 99.9999% confidence level. Tarnopolski (2016a) finds that for the similar Swift GRB dataset consisting of 947 GRBs, three groups are favored in the observer frame, whereas two in the rest frame.…”

A comparison of the gamma-ray bursts detected by BATSE and Swift

“…Horváth et al (2008) confirmed the existence of a third subgroup in the Swift dataset by applying the maximum likelihood (ML) method. Our significance of between 2.52% and 5.41% is weaker than the 0.46% significance obtained by Horváth et al (2008), as expected, because the ML method is a more robust statistical test. This is seen from new two studies, too: the ML test applied to the databases of RHESSI (Řípa et al 2009) and BeppoSAX (Horváth 2009) (Horváth 2009).…”

Analysis ofFermigamma-ray burst duration distribution

“…Swift also observed two local maxima, although with a prominent shoulder on the left side of the long GRB peak (Horváth et al 2008a), detected by means of the maximum log-likelihood method. Huja et al (2009) also obtained a bimodal distribution with a bump on one side of the long GRB peak, although somewhat weaker.…”

“…3). Previous works on datasets from BATSE (Horváth 1998(Horváth , 2002 and Swift (Horváth et al 2008a;Huja et al 2009) indicated that a three-Gaussian is a better fit than a corresponding two-Gaussian. On the other hand, a three-Gaussian fit to RHESSI (Řípa et al 2009) data yielded only a 93% probability of being correct compared to a two-Gaussian, meaning that there is a remarkable 7% probability that the log T 90 is well described by a two-Gaussian, while for BeppoSAX (Horváth 2009) the goodness-of-fit was not reported (only the maximum log-likelihoods).…”