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Uncovering Anomalous Gamma-Ray Bursts beyond Duration-based Classification

  • Authors: Shi-Qi Wei, Shuo Xiao, Yan-Qiu Zhang, Zheng-Huo Jiang, Tong-Lei Liao, Meng-Zhen Wang, Yi Wen, Dao-Yi Zhang, Shi-Jie Zhang, Xiang Li, Shao-Wei Xiong, Yang Zhang, Zi-Yi You, Wen-Jun Xiao

Shi-Qi Wei et al 2026 The Astrophysical Journal Letters 1003 .

  • Provider: AAS Journals

Caption: Figure 3.

Corner plot of the five parameters used in the t-SNE analysis: ﹩\mathrm{log}{\rm{\Delta }}{t}_{{\rm{\min }}}﹩, ﹩\mathrm{log}{F}_{64}﹩, ﹩\mathrm{log}{S}_{\gamma }﹩, ﹩\mathrm{log}{E}_{{\rm{p}}}﹩, and α. The lower triangle shows the pairwise distributions of the GRB sample, while the upper triangle presents the corresponding Pearson correlation coefficients. Gray points represent GRBs whose machine-learning classification is consistent with the traditional duration-based classification. Colored points mark anomalous bursts for which the machine-learning classification disagrees with the conventional T90 criterion.

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