https://doi.org/10.1140/epjc/s10052-023-11170-1
Regular Article - Theoretical Physics
Identifying hadronic molecular states with a neural network
1
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
Received:
28
June
2022
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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