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

机译:使用鲁棒模型 - 比较技术进行伽马射线突发持续时间的分类

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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 FermiGBM. We find that the Swift GRB distributions (in the observer frame) for the entire dataset favor three categories at about 2.4 sigma 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.
机译:伽马射线突发(GRB)通常分叉分为两个不同的类别,称为“短”和“长”,具体取决于它们的持续时间分别小于或大于两秒钟。然而,许多作者已经指出存在第三类GRB,其中短期和长GRB之间的平均持续时间中间。在这里,我们应用多种模型比较技术来验证这些索赔。对于每个类别,我们通过基于两个(或三个)对数分布的加权叠加来最大化似然函数来获得最佳拟合参数。然后,我们通过比较Chi-Square概率,Akaike信息标准(AIC)和贝叶斯信息标准(BIC)来在每个假设之间进行模型 - 比较。我们将这些技术统一地将这些技术从Swift(Observer和Untilsic帧),Batse,Bepposax和Fermigbm应用于GRBS。我们发现SWIFT GRB分布(在观察者框架中)为整个数据集有利于从Chi-Squares的差异的大约2.4 sigma的三类,并且显示了使用AIC和BIC的三个组件的决定性证据。然而,当具有测量红移的SWIFT GRBS子集进行相同的分析时,两个组分有利于边际意义。对于所有其他数据集,三个组件的证据是非常边缘或不受欢迎的。

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