A new Machine Learning algorithm for shower-head identification in the NeuLAND neutron detector is presented. The new algorithm uses densely-connected Deep Neural Networks (DNNs) to properly classify events and clusters, which allows accurate reconstruction of the 4-momenta of the detected neutrons. As data-events recorded with NeuLAND vary quite a lot in size, and not all emitted neutrons always produce signals in the detector, careful pre- and post-processing of the data turned out to be required for letting the DNNs be successful in their classifications. However, after properly implementing these procedures, the new algorithm offers a better efficiency than previously-used algorithms in virtually all investigated scenarios. However, the newly-developed algorithm (as well as previous ones) suffers from systematic uncertainties. These uncertainties mainly arise from the physics lists used in the Geant4 simulations to train the DNNs. They are particularly large for the neutron energy range around 200 MeV and for NeuLAND configurations of few double-planes (slimmed down version of the detector). The accuracy improves with a larger number of double-planes. Furthermore, both model improvements and accurate benchmarks are needed for the currently used Geant4 physics lists to reduce the systematic uncertainties of the new algorithm for high-precision studies. Further improvement of the present DNN algorithm is also needed, especially for experiments that require high precision in the neutron scattering angle reconstruction. However, it seems unlikely that this improvement can be realized using only NeuLAND data.

提出了一种新的机器学习算法，用于在NeuLAND中子探测器中识别喷头。新算法使用密集连接的深度神经网络（DNN）对事件和聚类进行正确分类，从而可以准确地重建检测到的中子的4动量。由于用NeuLAND记录的数据事件的大小变化很大，并且并非所有发出的中子总是在探测器中产生信号，因此要使DNN成功进行分类，需要对数据进行仔细的预处理和后处理。但是，在正确实施这些过程之后，新算法在几乎所有调查的场景中都比以前使用的算法提供了更高的效率。但是，新开发的算法（以及以前的算法）存在系统不确定性。这些不确定性主要来自Geant4模拟中用于训练DNN的物理列表。对于200 MeV左右的中子能量范围和很少有双平面的NeuLAND配置（探测器的缩小版），它们特别大。大量的双平面可提高精度。此外，当前使用的Geant4物理列表需要模型改进和准确基准，以减少用于高精度研究的新算法的系统不确定性。还需要对本发明的DNN算法进行进一步的改进，特别是对于在中子散射角重构中需要高精度的实验。但是，仅使用NeuLAND数据似乎无法实现这种改进。