Statistical study of observed and intrinsic durations among BATSE and Swift/BAT GRBs

Zitouni, Hannachi; Guessoum, Nidhal; Azzam, Walid J.; Mochkovitch, R.

doi:10.1007/s10509-015-2311-x

Cited by 56 publications

(94 citation statements)

References 39 publications

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“…As the sum of two normal distributions is skewed, which is the apparent case in the Fermi sample, the underlying multimodal distribution may not necessarily be composed of Gaussians. Recently, Zitouni et al (2015) suggested that the duration distribution corresponding to the collapsar scenario (associated with long GRBs) might not necessarily be symmetrical because of a non-symmetrical distribution of envelope masses of the progenitors.…”

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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“…AIC and BIC have also been previously used for GRB classification by a number of authors (Mukherjee et al 1998;Tarnopolski 2016a,b;Yang et al 2016;Zhang et al 2016b). Frequentist model comparison after binning the data has been used by Zitouni et al (2015); Tarnopolski (2015). More information about AIC and BIC and its application to a variety of astrophysical problems can be found in Liddle (2004Liddle ( , 2007; Tan & Biswas (2012); Shi et al (2012);Shafer (2015).…”

Section: Model Comparisonmentioning

confidence: 99%

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

Kulkarni

Desai

2017

Astrophys Space Sci

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Gamma-Ray Bursts (GRBs) have been conventionally bifurcated into two distinct categories dubbed "short" and "long", depending on whether their durations are less than or greater than two seconds respectively. However, many authors have pointed to the existence of a third class of GRBs with mean durations intermediate between the short and long GRBs. Here, we apply multiple model comparison techniques to verify these claims. For each category, we obtain the best-fit parameters by maximizing a likelihood function based on a weighted superposition of two (or three) lognormal distributions. We then do model-comparison between each of these hypotheses by comparing the chi-square probabilities, Akaike Information criterion (AIC), and Bayesian Information criterion (BIC). We uniformly apply these techniques to GRBs from Swift (both observer and intrinsic frame), BATSE, BeppoSAX, and Fermi-GBM. We find that the Swift GRB distributions (in the observer frame) for the entire dataset favor three categories at about 2.4σ from difference in chi-squares, and show decisive evidence in favor of three components using both AIC and BIC. However, when the same analysis is done for the subset of Swift GRBs with measured redshifts, two components are favored with marginal significance. For all the other datasets, evidence for three components is either very marginal or disfavored.

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“…Zhang & Choi (2008) analysed the duration distribution of 95 redshift-known Swift GRBs and found that the intrinsic duration is still bimodal. Zitouni et al (2015) found that 3 groups were statistically better that 2 groups after studying the intrinsic duration distribution of a larger datasets redshift-known Swift GRBs, which contains 248 GRBs. Tarnopolski (2016a) analysed the distribution of 947 Swift GRBs' observed duration and 347 redshift-known GRBs' duration in both observer and rest frames.…”

Section: Introductionmentioning

confidence: 96%

Two dimensional classification of the Swift/BAT GRBs

Yang¹,

Zhang²,

Jiang³

2016

Astrophys Space Sci

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Using Gaussian Mixture Model and Expectation Maximization algorithm, we have performed a density estimation in the framework of T 90 versus hardness ratio for 296 Swift/BAT GRBs with known redshift. Here, Bayesian Information Criterion has been taken to compare different models. Our investigations show that two instead of three or more Gaussian components are favoured in both the observer and rest frames. Our key findings are consistent with some previous results.

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Statistical study of observed and intrinsic durations among BATSE and Swift/BAT GRBs

Cited by 56 publications

References 39 publications

Analysis ofFermigamma-ray burst duration distribution

Analysis ofFermigamma-ray burst duration distribution

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

Two dimensional classification of the Swift/BAT GRBs

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