NeuLAND, the New Large Area Neutron Detector, is a key component to investigate the origin of matter in the universe with experimental nuclear physics. It is a core component of the Reactions with Relativistic Radioactive Beams setup at the Facility for Antiproton and Ion Research, Germany. Neutrons emitted from these reactions create a wide range of patterns in NeuLAND. From these patterns, the number of neutrons (multiplicity) and their first interaction points must be reconstructed to determine the neutrons’ four-momenta. In this paper, we detail the challenges involved in this reconstruction and present a range of possible solutions. Scikit-Learn classification models and simple Keras-based neural networks were trained on a wide range of input-scaler combinations and compared to classical models. While the improvement in multiplicity reconstruction is limited due to the overlap between features, the machine learning methods achieve a significantly better first interaction point selection, which directly improves the resolution of physical quantities.

NeuLAND 是新型大面积中子探测器，是利用实验核物理研究宇宙中物质起源的关键部件。它是德国反质子和离子研究设施的相对论放射性束反应的核心组成部分。这些反应释放的中子在 NeuLAND 中产生了广泛的模式。从这些模式中，必须重建中子的数量（多重性）和它们的第一个相互作用点，以确定中子的四动量。在本文中，我们详细介绍了这种重建所涉及的挑战，并提出了一系列可能的解决方案。Scikit-Learn 分类模型和简单的基于 Keras神经网络接受了广泛的输入缩放器组合的训练，并与经典模型进行了比较。虽然由于特征之间的重叠，多重性重建的改进受到限制，但机器学习方法实现了明显更好的第一交互点选择，这直接提高了物理量的分辨率。