JOURNAL ARTICLE

Evolution of the hot dense matter at LHC energies through light and heavy-flavor hadrons using non-extensive thermodynamics.

  • Published In: International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics, 2025, v. 40, n. 9. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Gyulai, László; Bíró, Gábor; Vértesi, Róbert; Barnaföldi, Gergely Gábor 3 of 3

Abstract

We investigate the formation and evolution of hot systems comprising charmed and light hadrons using non-extensive thermodynamics. We analyze data from pp, p–Pb, and Pb–Pb collisions at center-of-mass energies ranging from s NN = 2. 7 6 TeV to 13 TeV, measured by the CERN LHC ALICE experiment. The hadron species examined include charged pions and kaons, K s 0 , (anti)protons, ϕ mesons, Λ 0 hyperons, and D mesons. Employing our previously established methods, we determine the common Tsallis parameters T eq and q eq for each hadron type. While charm comes from earlier than light hadrons, we see that T eq is ordered by mass, reflecting a similar ordering in the time-scale relevant for the spectrum. Our results also allow for constraining the heat capacity of the system. The current analysis thus enhances our understanding of hadron production dynamics and thermal properties in high-energy collisions. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics. 2025/03, Vol. 40, Issue 9, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2025
  • ISSN:0217-751X
  • DOI:10.1142/S0217751X2444010X
  • Accession Number:184145772
  • 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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