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LUNCH: A Lightweight Unified Deep Learning Framework for General Transients Classification in High-energy Time-domain Astronomy

  • Authors: Peng Zhang, Chen-Wei Wang, Zheng-Hang Yu, Ren-Zhou Gui, Shao-Lin Xiong, Xiao-Bo Li, Li-Ming Song, Shi-Jie Zheng, Xiao-Yun Zhao, Yue Huang, Wang-Chen Xue, Ya-Qi Wang, Long-Bo Han, Jia-Cong Liu, Chao Zheng, Wen-Jun Tan, Sheng-Lun Xie, Ce Cai, Yan-Qiu Zhang, Hao-Xuan Guo, Yue Wang, Yang-Zhao Ren

Peng Zhang et al 2026 The Astrophysical Journal Supplement Series 286 .

  • Provider: AAS Journals

Caption: Figure 1.

Representative samples of the five trigger classes with Grad-CAM feature visualizations. For each event, the top panel shows the normalized multichannel (128 energy bins) count map, the middle panel shows the summed light curve across all energy channels, and the bottom panel displays the Grad-CAM heatmap, highlighting the spatiotemporal features (time–energy bins) that most influenced the model’s classification.

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