Study of exotic hadrons with machine learning

Abstract

We analyzed the invariant mass spectrum of near-threshold exotic states for one-channel candidates with a deep neural network. It can extract the scattering length and effective range, which would shed light on the nature of given states, from the experimental mass spectrum. As an application, the mass spectrum of the $X(3872)$ and the $T_{cc}^{+}$ are studied. The obtained scattering lengths, effective ranges, and most relevant thresholds are consistent with those from fitting to the experimental data. The advantage of the neural network is that it is more stable than the fitting, especially for low-statistic data. The network, which provides another way to analyze the experimental data, can also be applied to other one-channel near-threshold exotic candidates.

arXiv comment10 pages, 9 figures

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Cited by in corpus (4)

• An updated review of the new hadron states
• Strongly Bound Dibaryon with Maximal Beauty Flavor from Lattice QCD
• Revealing the nature of hidden charm pentaquarks with machine learning
• Rediscovery of Numerical Lüscher's Formula from the Neural Network