The Electron-Tracking Compton Camera (ETCC), which is a complete Compton camera that tracks Compton scattering electrons with a gas micro time projection chamber, is expected to open up MeV gamma-ray astronomy. The technical challenge for achieving several degrees of the point-spread function is precise determination of the electron recoil direction and the scattering position from track images. We attempted to reconstruct these parameters using convolutional neural networks. Two network models were designed to predict the recoil direction and the scattering position. These models marked 41$^\circ$ of angular resolution and 2.1 mm of position resolution for 75 keV electron simulation data in argon-based gas at 2 atm pressure. In addition, the point-spread function of the ETCC was improved to 15$^\circ$ from 22$^\circ$ for experimental data from a 662 keV gamma-ray source. The performance greatly surpassed that using traditional analysis.

中文翻译：

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用于电子跟踪康普顿相机的卷积神经网络的开发

电子跟踪康普顿相机（ETCC）是一个完整的康普顿相机，它使用气体微时间投影室跟踪康普顿散射电子，预计将开启 MeV 伽马射线天文学。实现多度点扩散函数的技术挑战是从轨道图像中精确确定电子反冲方向和散射位置。我们尝试使用卷积神经网络重建这些参数。设计了两个网络模型来预测反冲方向和散射位置。对于 2 atm 压力下的氩基气体中的 75 keV 电子模拟数据，这些模型的角度分辨率为 41$^\circ$，位置分辨率为 2.1 mm。此外，对于来自 662 keV 伽马射线源的实验数据，ETCC 的点扩散函数从 22$^\circ$ 提高到 15$^\circ$。性能大大超过使用传统分析。