Revealing the nature of hidden charm pentaquarks with machine learning.

Sci Bull (Beijing)

Helmholtz-Institut für Strahlen- und Kernphysik and Bethe Center for Theoretical Physics, Universität Bonn, Bonn D-53115, Germany; Institute for Advanced Simulation, Institut für Kernphysik and Jülich Center for Hadron Physics, Forschungszentrum Jülich, Jïlich D-52425, Germany; Tbilisi State University, Tbilisi 0186, Georgia. Electronic address:

Published: May 2023

We study the nature of the hidden charm pentaquarks, i.e., the P4312,P4440 and P(4457), with a neural network approach in pionless effective field theory. In this framework, the normal χ fitting approach cannot distinguish the quantum numbers of the P(4440) and P(4457). In contrast to that, the neural network-based approach can discriminate them, which still cannot be seen as a proof of the spin of the states since pion exchange is not considered in the approach. In addition, we also illustrate the role of each experimental data bin of the invariant J/ψp 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.

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http://dx.doi.org/10.1016/j.scib.2023.04.018DOI Listing

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