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

Huja, D.; Mészáros, A.; Řípa, J.

doi:10.1051/0004-6361/200809802

Cited by 51 publications

(64 citation statements)

References 21 publications

(49 reference statements)

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“…It was supported (Horváth 2002) with the use of the complete BATSE dataset. Evidence for a third normal component was also found in Swift data Zhang and Choi 2008;Huja et al 2009). BeppoSAX dataset was shown to be in agreement with earlier results regarding the bimodal distribution, and the detection of an intermediate-duration component was established on a lower, compared to BATSE and Swift, significance level due to a less populate sample (Horváth 2009).…”

Section: Introductionsupporting

confidence: 89%

On the limit between short and long GRBs

Tarnopolski

2015

Astrophys Space Sci

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Two classes of GRBs have been identified thus far without doubt and are prescribed to different physical scenarios-NS-NS or NS-BH mergers, and collapse of massive stars, for short and long GRBs, respectively. The existence of two distinct populations was inferred through a bimodal distribution of the observed durations T 90 , and the commonly applied 2 s limit between short and long GRBs was obtained by fitting a parabola between the two peaks in binned data from BATSE 1B. Herein, by means of a maximum likelihood (ML) method a mixture of two Gaussians is fitted to the datasets from BATSE, Swift, BeppoSAX, and Fermi in search for a local minimum that might serve as a new, more proper, limit for the two GRB classes. It is found that Swift and BeppoSAX distributions are unimodal, hence no local minimum is present, Fermi is consistent with the conventional limit, whereas BATSE gives the limit significantly longer (equal to 3.38 ± 0.27 s) than 2 s. These new values change the fractions of short and long GRBs in the samples examined, and imply that the observed T 90 durations are detector dependent, hence no universal limiting value may be applied to all satellites due to their different instrument specifications. Because of this, and due to the strong overlap of the two-Gaussian components, the straightforward association of short GRBs to mergers and long ones to collapsars is ambiguous.

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Section: Introductionsupporting

confidence: 89%

On the limit between short and long GRBs

Tarnopolski

2015

Astrophys Space Sci

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“…Horváth (1998) and Mukherjee et al (1998) independently discovered a third peak in the duration distribution in the BATSE 3B catalog (Meegan et al 1996), located between the short and long peaks, and the statistical existence of this intermediate class was supported (Horváth 2002) with the use of BATSE 4B data. Evidence for a third component in log T 90 was also found in the Swift data (Horváth et al 2008a,b;Zhang & Choi 2008;Huja et al 2009;Horváth et al 2010). Other datasets, i.e., RHESSI (Řípa et al 2009) and BeppoSAX (Horváth 2009), are both in agreement with earlier results regarding the bimodal distribution, and the detection of a third component was established on a lower significance level (compared to BATSE and Swift) owing to less populated samples.…”

Section: Introductionsupporting

confidence: 83%

“…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. The explanation may be that for the sample of 388 GRBs, a maximum log-likelihood method is more robust than applying a χ 2 fitting.…”

Section: Discussionmentioning

confidence: 85%

“…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%

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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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“…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.…”

Section: Introductionsupporting

confidence: 62%

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

Cited by 51 publications

References 21 publications

On the limit between short and long GRBs

On the limit between short and long GRBs

Analysis ofFermigamma-ray burst duration distribution

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

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