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Identifying Hadronic Molecular States with a Neural Network
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Identifying Hadronic Molecular States with a Neural Network
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.
Forward citations
Cited by 1 Pith paper
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Determination of the $Z_c(3900)$ and the $Z_{cs}(3985)$ states from joint analysis of experimental and lattice data
Joint analysis of experimental and lattice data confirms Z_c(3900) and Z_cs(3985) as SU(3) flavor partners with pole masses (3879.6 ± 4.8) MeV and (3976.9 ± 5.1) MeV, half-widths (32.2 ± 4.7) MeV and (28.8 ± 5.9) MeV,...
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