One of the main issues in hadron spectroscopy is to identify the origin of threshold or near-threshold enhancement. Prior to our study, there is no straightforward way of distinguishing even the lowest channel threshold-enhancement of the nucleon-nucleon system using only the cross-sections. The difficulty lies in the proximity of either a bound or virtual state pole to the threshold which creates an almost identical structure in the scattering region. Identifying the nature of the pole causing the enhancement falls under the general classification problem and supervised machine learning using a feed-forward neural network is known to excel in this task. In this study, we discuss the basic idea behind deep neural network and how it can be used to identify the nature of the pole causing the enhancement. The applicability of the trained network can be explored by using an exact separable potential model to generate a validation dataset. We find that within some acceptable range of the cut-off parameter, the neural network gives high accuracy of inference. The result also reveals the important role played by the background singularities in the training dataset. Finally, we apply the method to nucleon-nucleon scattering data and show that the network was able to give the correct nature of pole, i.e. virtual pole for \({}^1S_0\) partial cross-section and bound state pole for \({}^3S_0\).

强子光谱的主要问题之一是确定阈值或近阈值增强的起源。在我们的研究之前，没有直接的方法可以仅使用横截面来区分核子 - 核子系统的最低通道阈值增强。困难在于边界或虚拟状态极点靠近阈值，这会在散射区域中创建几乎相同的结构。识别导致增强的极点的性质属于一般分类问题，众所周知，使用前馈神经网络的监督机器学习在这项任务中表现出色。在这项研究中，我们讨论了深度神经网络背后的基本思想，以及如何使用它来识别导致增强的极点的性质。可以通过使用精确的可分离电位模型生成验证数据集来探索训练网络的适用性。我们发现在截止参数的某个可接受范围内，神经网络提供了较高的推理精度。结果还揭示了背景奇点在训练数据集中所起的重要作用。最后，我们将该方法应用于核子 - 核子散射数据，并表明该网络能够给出正确的极点性质，即虚拟极点\({}^1S_0\)部分横截面和\({}^3S_0\) 的束缚态极点。