Bibliographic Details
| Title: |
Low-energy Nϕ scattering from a pole-enhanced triangle diagram. |
| Authors: |
Yan, Mao-Jun1 (AUTHOR) yanmj0789@swu.edu.cn, An, Chun-Sheng1 (AUTHOR) ancs@swu.edu.cn, Deng, Cheng-Rong1 (AUTHOR) crdeng@swu.edu.cn |
| Source: |
Physics Letters B. Apr2026, Vol. 875, pN.PAG-N.PAG. 1p. |
| Subjects: |
Scattering (Physics), Feynman diagrams, Scattering (Mathematics), Threshold energy |
| Abstract: |
We investigate low-energy Nϕ scattering driven by a pole-enhanced triangle-like diagram, in which the two-Kaon-exchange contribution is promoted by the near-threshold Λ(1405) pole in the N K ¯ subsystem. Using an unphysical Kaon mass motivated by lattice simulations, we evaluate the Nϕ scattering length and find that this mechanism generates an attractive interaction with a magnitude of − 1.1 to − 0.5 fm. Spin-dependent effects are not treated explicitly and are expected to provide subleading corrections in the near-threshold region. We further analyze the low-energy behavior of the triangle-like diagram amplitude and show that the scattering length depends on the parameter δ , defined as the mass difference between the K K ¯ threshold and the ϕ meson, and the pole position of Λ(1405), where the Λ(1405) plays a crucial role to understand Nϕ interaction. Furthermore, by employing physical hadron masses, our calculated scattering length is found to be consistent with current experimental data, providing a unified description across both unphysical and physical mass regimes. This type of interaction differs from that associated with van der Waals-type forces or the long-range tail of two-pion exchange, highlighting the role of three-body dynamics encoded in the pole-enhanced triangle-like diagram in shaping the near-threshold Nϕ interaction. [ABSTRACT FROM AUTHOR] |
|
Copyright of Physics Letters B is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |