论文标题
与机器学习的异国黑龙的研究
Study of exotic hadrons with machine learning
论文作者
论文摘要
我们分析了具有深层神经网络的单渠道候选者的近阈值外来状态的不变质谱。它可以从实验质谱中提取散射长度和有效范围，从而阐明给定状态的性质。作为应用程序，研究了$ x（3872）$和$ t_ {cc}^+$的质谱。获得的散射长度，有效范围和最相关的阈值与从拟合到实验数据的阈值一致。神经网络的优点是它比拟合更稳定，尤其是对于低统计数据。该网络提供了另一种分析实验数据的方法，也可以应用于其他接近阈值外来候选人的单渠道。
We analyzed the invariant mass spectrum of near-threshold exotic states for one-channel candidates with a deep neural network. It can extract the scattering length and effective range, which would shed light on the nature of given states, from the experimental mass spectrum. As an application, the mass spectrum of the $X(3872)$ and the $T_{cc}^+$ are studied. The obtained scattering lengths, effective ranges, and most relevant thresholds are consistent with those from fitting to the experimental data. The advantage of the neural network is that it is more stable than the fitting, especially for low-statistic data. The network, which provides another way to analyze the experimental data, can also be applied to other one-channel near-threshold exotic candidates.
