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Identifying Hadronic Molecular States with a Neural Network

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arxiv 2205.03572 v2 pith:X2EYXN74 submitted 2022-05-07 hep-ph

Chang Chen , Hao Chen , Wen-Qi Niu , Han-Qing Zheng This is my paper · ORCID
classification hep-ph
keywords molecularstatehadronicneuralaccordingapplicationsappliedchannel
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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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