Event reconstruction of Compton telescopes using a multi-task neural network
Event reconstruction of Compton telescopes using a multi-task neural network
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DOI：
10.1016/j.nima.2022.166897
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
S. Takashima;H. Odaka;H. Yoneda;Y. Ichinohe;A. Bamba;T. Aramaki;Y. Inoue
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文献类型：
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作者：
S. Takashima;H. Odaka;H. Yoneda;Y. Ichinohe;A. Bamba;T. Aramaki;Y. Inoue
我们已经开发了一个神经网络模型进行事件重建的康普顿望远镜。该模型重建由检测器中的三个或更多交互组成的事件。对于康普顿望远镜来说，确定伽马射线相互作用的时间顺序以及入射光子是否将所有能量储存在探测器中或从探测器中逃逸是至关重要的。我们的模型使用具有三个完全连接节点的隐藏层的多任务神经网络同时预测这两个基本因素。为了验证，我们进行了数值实验，使用蒙特卡罗模拟，假设一个大面积的康普顿望远镜使用液态氩测量伽马射线的能量高达3.0兆电子伏。重建模型表现出良好的性能的事件重建的多次散射事件，包括多达八个命中。对于4 π各向同性光子的八次击中事件，命中顺序预测的准确度约为60%，而逃逸标志的准确度高于70%。与其他两种算法相比，一个经典的模型和基于物理的概率的，本神经网络方法显示出高性能的估计精度，特别是当散射的数量很小，3或4。由于仿真数据很容易优化网络模型，该模型可以灵活地应用于各种康普顿望远镜。
We have developed a neural network model to perform event reconstruction of Compton telescopes. This model reconstructs events that consist of three or more interactions in a detector. It is essential for Compton telescopes to determine the time order of the gamma-ray interactions and whether the incident photon deposits all energy in a detector or it escapes from the detector. Our model simultaneously predicts these two essential factors using a multi-task neural network with three hidden layers of fully connected nodes. For verification, we have conducted numerical experiments using Monte Carlo simulation, assuming a large-area Compton telescope using liquid argon to measure gamma rays with energies up to 3.0 MeV. The reconstruction model shows excellent performance of event reconstruction for multiple scattering events that consist of up to eight hits. The accuracies of hit order prediction are around 60% while those of escape flags are higher than 70% for up to eight-hit events of 4 π isotropic photons. Compared with two other algorithms, a classical model and a physics-based probabilistic one, the present neural network method shows high performance in estimation accuracy particularly when the number of scattering is small, 3 or 4. Since simulation data easily optimize the network model, the model can be flexibly applied to a wide variety of Compton telescopes.