Regular Article - Theoretical Physics
Identifying hadronic molecular states with a neural network
Department of Physics and State Key Laboratory of Nuclear Physics and Technology, Peking University, 100871, Beijing, China
2 College of Physics, Sichuan University, 610065, Chengdu, Sichuan, China
Accepted: 30 December 2022
Published online: 21 January 2023
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 . The results show that should be regarded as a molecular state but X(3872) not. As for X(4260), it can not be a molecular state of . Some discussions on are also provided.
© The Author(s) 2023
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