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 ${\bar{D}}^{\ast }D$ 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.

Revised version published in EPJC, one author is added

References in corpus (6)

• Precise measurement of the $e^{+}{e}^{-}\to {\pi }^{+}{\pi }^{-}J/\psi$ cross section at center-of-mass energies from 3.77 to 4.60 GeV
• Ambiversion of X(3872)
• Cross section measurements of $e^{+}{e}^{-}\to \omega {\chi }_{c0}$ from $\sqrt{s}=$4.178 to 4.278 GeV
• Model independent analysis of coupled-channel scattering: a deep learning approach
• X_1(2900) as a D_1 K molecule
• Classifying near-threshold enhancement using deep neural network

Cited by in corpus (3)

• The $X(6900)$ peak could be a molecular state
• ${\bar{B}}^0\to {\bar{K}}^{(\ast )0}X$, $B^{-}\to K^{(\ast )-}X$, ${\bar{B}}_s^0\to \eta ({\eta }^{\prime },\varphi )X$ from the $X(3872)$ molecular perspective
• Predicting charmed-strange molecular tetraquarks with $K^{(\ast )}$ and $T$-doublet charmed or anticharmed meson