Revealing the nature of hidden charm pentaquarks with machine learning

Abstract

We study the nature of the hidden charm pentaquarks, i.e. the $P_c(4312)$, $P_c(4440)$ and $P_c(4457)$, with a neural network approach in pionless effective field theory. In this framework, the normal ${\chi }^2$ fitting approach cannot distinguish the quantum numbers of the $P_c(4440)$ and $P_c(4457)$. In contrast to that, the neural network-based approach can discriminate them. In addition, we also illustrate the role of each experimental data bin of the invariant $J/\psi p$ mass distribution on the underlying physics in both neural network and fitting methods. Their similarities and differences demonstrate that neural network methods can use data information more effectively and directly. This study provides more insights about how the neural network-based approach predicts the nature of exotic states from the mass spectrum.

accepted by Science Bulletin

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Cited by in corpus (6)

• Chiral perturbation theory for heavy hadrons and chiral effective field theory for heavy hadronic molecules
• Spectrum of the molecular pentaquarks
• Unravelling Pentaquarks with Born--Oppenheimer effective theory
• Towards modeling the short-range interactions of hidden/open charm pentaquark molecular states
• Interpretation of near-threshold peaks using the method of independent S-matrix poles
• Learning Pole Structures of Hadronic States using Predictive Uncertainty Estimation