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arXiv · 2205.03572

Identifying Hadronic Molecular States with a Neural Network

Chang Chen·Hao Chen·Wen-Qi Niu·Han-Qing Zheng
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Abstract

Neural networks are trained to judge whether or not an exotic state is a hadronic molecule of a given channel according its line-shapes. This method performs well in both trainings and validation tests. As applications, it is applied to study $X(3872)$, $X(4260)$ and $Z_c(3900)$. The results show that $Z_c(3900)$ should be regarded as a $\bar{D}^* D$ molecular state but $X(3872)$ not. As for $X(4260)$, it can not be a molecular state of $\chi_{c0}\omega$. Some discussions on $X_1(2900)$ are also provided.

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Chang Chen, Hao Chen, Wen-Qi Niu, Han-Qing Zheng. 2022-05-07. Identifying Hadronic Molecular States with a Neural Network. https://doi.org/10.1140/epjc/s10052-023-11170-1

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