AI 中文总结
该研究针对超快光子堆积导致的光谱信息丢失问题，提出基于位置敏感探测器空间能量沉积模式的机器学习方法，可恢复信号占比与光子多重性，性能优于传统方法，有望用于光子主动询问应用。
AI 中文摘要
我们提出了从多个并发光子相互作用中恢复光谱信息的方法，这些信息通常会因脉冲堆积而丢失。特别地，我们聚焦于基于位置敏感探测器中的空间（而非时间）能量沉积模式来恢复信息的机器学习方法。我们构建了两个代表性问题：(1) 恢复来自单能信号与平滑背景的总能量沉积占比；(2) 在纯源项示例中恢复信号多重性，即相互作用光子的数量。在信号占比恢复问题中，我们使用3D卷积神经网络（CNNs）、全连接神经网络（FCNNs）、基于PointNet++架构的网络，以及两种非机器学习方法，在合成数据中估计信号占比，此时存在多达20个总堆积光子。当训练数据集适合内存时，CNN、FCNN和PointNet++模型重建信号能量沉积占比的均方根误差（RMSE）分别为14.5%、18.8%和16.8%，而经典方法表现不佳，且不会随额外训练数据提升性能。在多重性恢复问题中，我们证明，当使用合成堆积的真实Cs-137数据训练时，3D CNN架构能够以亚光子级RMSE恢复多重性，优于非机器学习基线。这些方法可适配未来更具体的光子主动询问应用，有助于在这些领域重新启用光谱分析。
英文摘要
We present methods for recovering spectroscopic information from multiple concurrent photon interactions that would normally be lost due to pulse pileup. In particular, we focus on machine learning methods to recover information based on spatial (rather than temporal) energy deposition patterns in position-sensitive detectors. We construct two representative problems, namely (1) recovering the fraction of total energy deposition stemming from a monoenergetic signal vs. a smooth background; and (2) recovering the signal multiplicity, i.e., the number of interacting photons, in a pure-source-term example. In the signal fraction recovery problem, we use 3D convolutional neural networks (CNNs), fully-connected neural networks (FCNNs), a network based on the PointNet++ architecture, and two non-machine-learning methods to estimate the signal fraction in synthetic data when up to 20 total piled-up photons are present. The CNN, FCNN, and PointNet++ models reconstruct the signal energy deposition fractions with root mean square errors (RMSEs) of $14.5\%$, $18.8\%$, and $16.8\%$ given training datasets that fit in-core, while the classical methods perform poorly and will not improve with additional training data. In the multiplicity recovery problem, we demonstrate that, when trained with synthetically-piled-up real Cs-137 data, the 3D CNN architecture can recover the multiplicity with sub-photon RMSE, outperforming non-ML baselines. These methods can be adapted to future, more specific photon active interrogation applications, helping to re-enable spectroscopic analyses in those domains.
Comments14 pages, 12 figures, 4 tables
