论文标题
使用深神经网络对振幅的极点进行分类
Classifying Pole of Amplitude Using Deep Neural Network
论文作者
论文摘要
过去十年中观察到的大多数外来共振出现在某个阈值附近。这些接近阈值的现象可以解释为真正的共振状态或增强的阈值尖端。显然，没有直接的方式来区分这两个结构。在这项工作中，我们利用深层馈送神经网络的力量在对物体几乎具有相似特征的对象进行分类中。我们构建了一个具有散射幅度的神经网络模型，作为极点的输入和性质，导致增强作为输出。培训数据是由满足单位性和分析性要求的S-矩阵生成的。使用可分离的潜在模型，我们生成一个验证数据集来衡量网络的预测能力。我们发现，当验证数据的截止参数在$ 400 $ - $ 800 \ mbox {Mev} $之内时，我们训练有素的神经网络模型可提供高精度。作为最终测试，我们使用nijmegen的部分波和核子核子散射的潜在模型，并表明网络给出了POL的正确性质。
Most of exotic resonances observed in the past decade appear as peak structure near some threshold. These near-threshold phenomena can be interpreted as genuine resonant states or enhanced threshold cusps. Apparently, there is no straightforward way of distinguishing the two structures. In this work, we employ the strength of deep feed-forward neural network in classifying objects with almost similar features. We construct a neural network model with scattering amplitude as input and nature of pole causing the enhancement as output. The training data is generated by an S-matrix satisfying the unitarity and analyticity requirements. Using the separable potential model, we generate a validation data set to measure the network's predictive power. We find that our trained neural network model gives high accuracy when the cut-off parameter of the validation data is within $400$-$800\mbox{ MeV}$. As a final test, we use the Nijmegen partial wave and potential models for nucleon-nucleon scattering and show that the network gives the correct nature of pole.
