Classifying the pole of an amplitude using a deep neural network
Classifying the pole of an amplitude using a deep neural network
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使用深度神经网络对振幅的极点进行分类
DOI：
10.1103/physrevd.102.016024
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发表时间：
2020
影响因子：
5
通讯作者：
Atsushi Hosaka
中科院分区：
文献类型：
--
作者：
Denny Lane B. Sombillo;Yoichi Ikeda;Toru Sato;Atsushi Hosaka
在过去的十年中观察到的大多数奇异共振出现在某个阈值附近的峰值结构。这些近阈值现象可以解释为真正的共振态或增强的阈值尖点。显然，没有直接的方法来区分这两种结构。在这项工作中，我们利用深度前馈神经网络的优势对具有几乎相似特征的对象进行分类。我们构造了一个神经网络模型，以散射振幅作为输入，引起增强的极点的性质作为输出。训练数据由满足么正性和分析性要求的S矩阵生成。使用可分离势模型，我们生成了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 MeV之间时，我们训练的神经网络模型具有很高的精度。作为最后的测试，我们使用奈梅亨分波和潜在的核子核子散射模型，并表明该网络给出了正确的性质的极点。
Most of the exotic resonances observed in the past decade appear as a 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 the nature of a 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 cutoff parameter of the validation data is within 400–800 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 the pole.