JOURNAL ARTICLE

Production of bound states of magnetic monopoles in high-energy collisions at LHC.

  • Published In: International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics, 2024, v. 39, n. 11/12. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: da Silva, João Vitor B.; Sauter, Werner K. 3 of 3

Abstract

In this work, we present the conducted studies for the production of the monopolium at the LHC in ultraperipheral collisions, using beams of proton–proton (pp) and lead–lead (PbPb) nuclei. The monopolium is the bound state of a monopole–antimonopole pair. The study of the magnetic monopole in this characteristic state is justified due to the very strong coupling constant, which allows us to suggest that this exotic particle can be produced in the bound state. In this approach, the monopolium is described by a wave function, and its solution comes from the numerical method applied to the Schrödinger equation with a modified Cornell potential. The monopolium is produced by a photon fusion production mechanism, with the Weizsäcker–Williams and Drees–Zeppenfeld expressions to describe the lead and proton equivalent photon distributions. We estimate a high-production rate of monopolium for pp collisions with s = 1 4 TeV and PbPb collisions with s = 5. 5 TeV at the LHC. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics. 2024/04, Vol. 39, Issue 11/12, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:0217-751X
  • DOI:10.1142/S0217751X24500489
  • Accession Number:178097683
  • Copyright Statement:Copyright of International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics is the property of World Scientific Publishing Company 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.