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English(EN) Machine Learning-Based Reconstruction for Resistive Silicon Sensors

机器学习增强高能物理硅传感器数据重建

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了机器学习技术，以改进高能物理中使用的阻性硅传感器的全波形数据重建和压缩。该研究探讨了循环神经网络（特别是LSTM层）用于全波形重建，并考虑了其在现场可编程门阵列（FPGA）上的部署。此外，该工作还研究了基于Transformer的拓扑无关架构，旨在保持高位置分辨率并指导未来的传感器设计。 AI

影响 这项研究可能导致高能物理实验探测器的数据处理效率更高，空间分辨率得到改善。

排序理由 该集群包含一篇详细介绍新研究方法的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

机器学习增强高能物理硅传感器数据重建

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Alexander Aoki, Gaetano Barone, Leena Diehl, Gabriele Giacomini, Vagelis Gkougkousis, Hanshal Goyal, Rohan Kher, Daniel Li, Anna Macchiolo, Yevhenii Padnuik, Daria Senina, Samantha Sunnarborg, Jessica Tang, Alessandro Tricoli, Lixing Wang, Don C. Wong ·

    基于机器学习的电阻式硅传感器的重建

    arXiv:2607.11585v1 Announce Type: cross Abstract: Low-Gain Avalanche Diodes (LGADs) and AC-coupled Low-Gain Avalanche Diodes (AC-LGADs) are promising technologies for precision timing and four-dimensional tracking. In AC-LGADs, the AC pad is coupled to the resistive n$^{+}$ layer…

  2. arXiv cs.LG TIER_1 English(EN) · Don C. Wong ·

    基于机器学习的电阻式硅传感器的重建

    Low-Gain Avalanche Diodes (LGADs) and AC-coupled Low-Gain Avalanche Diodes (AC-LGADs) are promising technologies for precision timing and four-dimensional tracking. In AC-LGADs, the AC pad is coupled to the resistive n$^{+}$ layer through a dielectric layer, while the gain layer …

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