arXiv cs.AI· Junle Li, Zhaoxiang Wu, Guanda Gong, Zhaohan Li, Wuming Luo, Jiahui Wei, Wenxing Fang, Hehe Fan·· 12 小时前AI 评分18
利用时空深度学习抑制大型液体闪烁体探测器中的 ¹⁴C 光子击中
Suppression of $^{14}\mathrm{C}$ photon hits in large liquid scintillator detectors via spatiotemporal deep learning
AI 导读
研究提出三种模型在命中层面标记 e⁺ 事件中与 ¹⁴C 堆积的 ¹⁴C 光子击中，包括门控时空图神经网络和两种分别采用标量与矢量电荷编码的 Transformer 模型。
正文 · 原文
Abstract:Liquid scintillator detectors are widely used in neutrino experiments due to their low energy threshold and high energy resolution. Despite the tiny abundance of $^{14}$C in LS, the photons induced by the $\beta$ decay of the $^{14}$C isotope inevitably contaminate the signal, degrading the energy resolution. In this work, we propose three models to tag $^{14}$C photon hits in e$^+$ events with $^{14}$C pile-up, thereby suppressing its impact on the energy resolution at the hit level: a gated spatiotemporal graph neural network and two Transformer-based models with scalar and vector charge encoding. For a simulated test dataset in which each event contains one $^{14}\mathrm{C}$ and one e$^+$ with kinetic energy below 5 MeV, the models achieve $^{14}\mathrm{C}$ recall rates of 25%-48% while maintaining $\mathrm{e}^{+}$ to $^{14}\mathrm{C}$ misidentification below 1%, leading to a large improvement in the resolution of total charge for events where $\mathrm{e}^{+}$ and $^{14}\mathrm{C}$ photon hits strongly overlap in space and time.
| Comments: | 14 pages, 11 figures. Accepted for publication in Physical Review D |
| Subjects: | Instrumentation and Detectors (physics.ins-det); Artificial Intelligence (cs.AI); High Energy Physics - Experiment (hep-ex) |
| Cite as: | arXiv:2603.27727 [physics.ins-det] |
| (or arXiv:2603.27727v2 [physics.ins-det] for this version) | |
| https://doi.org/10.48550/arXiv.2603.27727 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1103/6d6b-yfxx
DOI(s) linking to related resources |
Submission history
From: Junle Li [view email]
[v1]
Sun, 29 Mar 2026 15:03:42 UTC (15,870 KB)
[v2]
Fri, 16 Oct 2026 12:28:01 UTC (16,065 KB)
来源：arXiv cs.AI · arxiv.org