A Third Class of Gamma‐Ray Bursts?

Horváth, I.

doi:10.1086/306416

Cited by 174 publications

(192 citation statements)

References 7 publications

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“…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).…”

Section: Discussionmentioning

confidence: 99%

Analysis ofFermigamma-ray burst duration distribution

Tarnopolski

2015

A&A

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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.

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Section: Discussionmentioning

confidence: 99%

Analysis ofFermigamma-ray burst duration distribution

Tarnopolski

2015

A&A

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“…However, unless the space-time geometry has a very particular structure, the distribution of log f (z) cannot be Gaussian. This means that the Gaussian nature of the distribution of log T 90 must be dominated by the distribution 1 There is also an evidence for the existence of a third intermediate subgroup as part of the long duration group (Horváth 1998;Mukherjee et al 1998;Hakkila et al 2000a,c;Balastegui et al 2001;Horváth 2002), which shows a distinct sky angular distribution (Mészáros et al 2000a,b;Litvin et al 2001). We do not deal with this third group here.…”

Section: Analysis Of the Duration Distributionmentioning

confidence: 99%

On the difference between the short and long gamma-ray bursts

Balázs

Bagoly²,

Horváth³

et al. 2003

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Abstract. We argue that the distributions of both the intrinsic fluence and the intrinsic duration of the γ-ray emission in gammaray bursts from the BATSE sample are well represented by log-normal distributions, in which the intrinsic dispersion is much larger than the cosmological time dilatation and redshift effects. We perform separate bivariate log-normal distribution fits to the BATSE short and long burst samples. The bivariate log-normal behaviour results in an ellipsoidal distribution, whose major axis determines an overall statistical relation between the fluence and the duration. We show that this fit provides evidence for a power-law dependence between the fluence and the duration, with a statistically significant different index for the long and short groups. We discuss possible biases, which might affect this result, and argue that the effect is probably real. This may provide a potentially useful constraint for models of long and short bursts.

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“…We proceed in an identical way to the successful statistical analysis completed for the BATSE catalog (Horváth 1998) leading to the discovery of the third subgroup (Mukherjee et al 1998;Bagoly et al 1998;Horvath 1999;Hakkila et al 2000;Rajaniemi & Mähönen 2002;Horváth 2002Horváth , 2003Balázs et al 2003;Horváth et al 2006;Chattopadhyay et al 2007). A statistical study of the Swift database -using the maximum likelihood method -has already shown evidence of a third subgroup (Horváth et al 2008).…”

Section: Introductionmentioning

confidence: 98%

“…The χ 2 Tables 4 and 5 are only available in electronic form at http://www.aanda.org fitting was not used because of the smallness of the population. However, historically, the first evidence of the third subgroup in the BATSE database came from the simple χ 2 method (Horváth 1998), and the number of 388 data points not should be too small for this testing. In all cases, one has to probe this fitting also for the Swift data sample.…”

Section: Introductionmentioning

confidence: 99%

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

Huja¹,

Mészáros²,

Řípa³

2009

A&A

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Aims. The durations of 388 gamma-ray bursts detected by the Swift satellite, are analyzed statistically to search for subgroups. The results are then compared with results obtained earlier for data from the BATSE database. Methods. We apply the standard χ 2 test. Results. As for data in the BATSE database, short and long subgroups are also reliably identified in the Swift data. An intermediate subgroup is also seen in the Swift database.Conclusions. The analysis of the entire sample of 388 GRBs provides a support for the existence of three subgroups.

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A Third Class of Gamma‐Ray Bursts?

Cited by 174 publications

References 7 publications

Analysis ofFermigamma-ray burst duration distribution

Analysis ofFermigamma-ray burst duration distribution

On the difference between the short and long gamma-ray bursts

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

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