LUNCH: A Lightweight Unified Deep-Learning Framework for General Transients Classification in High-Energy Time-Domain Astronomy
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912797895426048 |
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| author | Zhang, Peng Wang, Chen-Wei Yu, Zheng-Hang Gui, Ren-Zhou Xiong, Shao-Lin Li, Xiao-Bo Song, Li-Ming Zheng, Shi-Jie Zhao, Xiao-Yun Huang, Yue Xue, Wang-Chen Wang, Ya-Qi Han, Long-Bo Liu, Jia-Cong Zheng, Chao Tan, Wen-Jun Xie, Sheng-Lun Cai, Ce Zhang, Yan-Qiu Guo, Hao-Xuan Wang, Yue Ren, Yang-Zhao |
| author_facet | Zhang, Peng Wang, Chen-Wei Yu, Zheng-Hang Gui, Ren-Zhou Xiong, Shao-Lin Li, Xiao-Bo Song, Li-Ming Zheng, Shi-Jie Zhao, Xiao-Yun Huang, Yue Xue, Wang-Chen Wang, Ya-Qi Han, Long-Bo Liu, Jia-Cong Zheng, Chao Tan, Wen-Jun Xie, Sheng-Lun Cai, Ce Zhang, Yan-Qiu Guo, Hao-Xuan Wang, Yue Ren, Yang-Zhao |
| contents | The increasing data volume of high-energy space monitors necessitates real-time, automated transient classification for multi-messenger follow-up. Conventional methods rely on empirical features like hardness ratios and reliable localization, which are not always precisely available during early detection. We developed the Lightweight Unified Neural Classifier for High-energy Transients (LUNCH) - an end-to-end deep-learning framework that performs general transient classification directly from raw multi-band light curves, eliminating the need for background subtraction or source localization. Its dual-scale architecture fuses long- and short-scale temporal evolution adaptively. Evaluated on 15 years of Fermi/GBM triggers, the optimal model achieves 97.23% accuracy when trained on complete energy spectra. A lightweight version using only three broad energy bands retains 95.07% accuracy, demonstrating that coarse spectral information fused with temporal context enables robust discrimination. The system significantly outperforms the GBM in-flight classifier on three months of independent test data. Feature visualization reveals well-separated class clusters, confirming physical interpretability. LUNCH combines high accuracy, low computational cost, and instrument-agnostic inputs, offering a practical solution for real-time in-flight processing that enables timely triggers for immediate multi-wavelength and multi-messenger follow-up observations in future time-domain missions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24764 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LUNCH: A Lightweight Unified Deep-Learning Framework for General Transients Classification in High-Energy Time-Domain Astronomy Zhang, Peng Wang, Chen-Wei Yu, Zheng-Hang Gui, Ren-Zhou Xiong, Shao-Lin Li, Xiao-Bo Song, Li-Ming Zheng, Shi-Jie Zhao, Xiao-Yun Huang, Yue Xue, Wang-Chen Wang, Ya-Qi Han, Long-Bo Liu, Jia-Cong Zheng, Chao Tan, Wen-Jun Xie, Sheng-Lun Cai, Ce Zhang, Yan-Qiu Guo, Hao-Xuan Wang, Yue Ren, Yang-Zhao Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena The increasing data volume of high-energy space monitors necessitates real-time, automated transient classification for multi-messenger follow-up. Conventional methods rely on empirical features like hardness ratios and reliable localization, which are not always precisely available during early detection. We developed the Lightweight Unified Neural Classifier for High-energy Transients (LUNCH) - an end-to-end deep-learning framework that performs general transient classification directly from raw multi-band light curves, eliminating the need for background subtraction or source localization. Its dual-scale architecture fuses long- and short-scale temporal evolution adaptively. Evaluated on 15 years of Fermi/GBM triggers, the optimal model achieves 97.23% accuracy when trained on complete energy spectra. A lightweight version using only three broad energy bands retains 95.07% accuracy, demonstrating that coarse spectral information fused with temporal context enables robust discrimination. The system significantly outperforms the GBM in-flight classifier on three months of independent test data. Feature visualization reveals well-separated class clusters, confirming physical interpretability. LUNCH combines high accuracy, low computational cost, and instrument-agnostic inputs, offering a practical solution for real-time in-flight processing that enables timely triggers for immediate multi-wavelength and multi-messenger follow-up observations in future time-domain missions. |
| title | LUNCH: A Lightweight Unified Deep-Learning Framework for General Transients Classification in High-Energy Time-Domain Astronomy |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2512.24764 |