Characterising and distinguishing gamma-ray bursts (GRBs) has interested astronomers for many decades. While some authors have found two or three groups of GRBs by analyzing only a few parameters, recent work identified five ellipsoidally-shaped groups upon considering nine parameters $T_{50}, T_{90}, F_1, F_2, F_3, F_4, P_{64}, P_{256}, P_{1024}$. Yet others suggest sub-classes within the two or three groups found earlier. Using a mixture model of Gaussian factor analysers, we analysed 1150 GRBs, that had nine parameters observed, from the current Burst and Transient Source Experiment (BATSE) catalogue, and again established five ellipsoidal-shaped groups to describe the GRBs. These five groups are characterised in terms of their average duration, fluence and spectrum as shorter-faint-hard, long-intermediate-soft, long-intermediate-intermediate, long-bright-intermediate and short-faint-hard. The use of factor analysers in describing individual group densities allows for a more thorough group-wise characterisation of the parameters in terms of a few latent features. However, given the discrepancy with many other existing studies that advocated for two or three groups, we also performed model-estimated overlap-based syncytial clustering (MOBSynC) that successively merges poorer-separated groups. The five ellipsoidal groups merge into three and then into two groups, one with GRBs of low durations and the other having longer duration GRBs. These groups are also characterised in terms of a few latent factors made up of the nine parameters. Our analysis provides context for all three sets of results, and in doing so, details a multi-layered characterisation of the BATSE GRBs, while also explaining the structure in their variability.

翻译：伽马射线暴（GRBs）的特征刻画与区分已吸引天文学家关注数十年。尽管部分学者通过分析少量参数发现了两到三个GRB群组，但近期研究在考虑九个参数（$T_{50}, T_{90}, F_1, F_2, F_3, F_4, P_{64}, P_{256}, P_{1024}$）后识别出五个椭球状群组。另有研究则建议在早期发现的两到三个群组内存在子类。本研究采用高斯因子分析混合模型，对当前爆发与暂现源实验（BATSE）星表中具有九个观测参数的1150个GRB进行分析，再次确立了五个椭球状群组来描述GRBs。这五个群组按其平均持续时间、辐射流与能谱特征被刻画为：短-暗-硬、长-中等-软、长-中等-中等、长-亮-中等以及短-暗-硬。通过因子分析模型描述各群组密度，能够以少数潜在特征更全面地刻画群组内参数特性。然而，鉴于与众多主张两到三个群组的现有研究存在分歧，我们还实施了基于模型估计重叠的融合聚类（MOBSynC），该方法通过逐步合并分离度较差的群组，将五个椭球群组先合并为三个，最终合并为两个群组：一个包含短时标GRBs，另一个包含长时标GRBs。这些群组同样通过由九个参数构成的少数潜在因子进行特征刻画。我们的分析为三组结果提供了统一解释框架，从而详细描绘了BATSE GRBs的多层次特征，同时阐明了其变异性的内在结构。