JOURNAL ARTICLE

Event-activity-dependent beauty-baryon enhancement in simulations with color junctions.

  • Published In: International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics, 2025, v. 40, n. 21. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Földvári, Lea Virág; Varga, Zoltán; Vértesi, Róbert 3 of 3

Abstract

Recent results from ALICE and CMS show a low-transverse-momentum enhancement of charm baryon-to-meson production ratios over model predictions based on e + e− collisions. Several mechanisms are proposed to understand this phenomenon. New measurements by the LHCb and ALICE experiments show a similar enhancement in the beauty sector. We explore this enhancement in terms of event activity using the color-reconnection beyond leading order approximation model. We propose sensitive probes relying on the event shape that will allow for the differentiation between the proposed beauty-production scenarios using freshly collected LHC Run-3 data, and we also compare these to predictions for charm. We confirm that flattenicity, a new event classifier is sensitive to the underlying event. Our results will contribute to a deeper theoretical understanding of the heavy-flavor baryon enhancement and its relation to baryon enhancement in general. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics. 2025/07, Vol. 40, Issue 21, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2025
  • ISSN:0217-751X
  • DOI:10.1142/S0217751X25420059
  • Accession Number:186254860
  • 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.)

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