JOURNAL ARTICLE

Study of thermal properties of the medium produced in Au+Au collisions at sNN = 19.6GeV using a thermal model.

  • Published In: International Journal of Modern Physics E: Nuclear Physics, 2024, v. 33, n. 12. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Bairathi, V.; Blaschke, D.; Bravina, L.; Brooks, W. K.; Chhabra, T.; Kabana, S.; Vitiuk, O.; Zabrodin, E.; Zherebtsova, E. 3 of 3

Abstract

In this paper, we present the thermal properties of the medium formed in ultra-relativistic heavy-ion collisions at chemical freeze-out using a thermal model. Experimental data of various hadron species from 0–5% most central Au + Au collisions at s N N = 1 9. 6 GeV from the STAR BES program are used to analyze the thermal properties, namely chemical freeze-out temperature, baryon chemical potential and strangeness chemical potential. A χ 2 minimization technique is employed to obtain thermal properties. We also obtain thermal properties with strangeness conversation condition and at zero potentials μ B ∕ T = μ S ∕ T = 0. We compared particle ratios from the thermal model with the experimental data. The thermal model describes particle ratios within ± 2 standard deviations and χ 2 ∕ NDF between 1–2. We discuss collision energy dependence of thermodynamic properties of the medium at freeze-out and compare results with the published STAR results and other thermal model calculations. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics E: Nuclear Physics. 2024/12, Vol. 33, Issue 12, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:0218-3013
  • DOI:10.1142/S0218301324420035
  • Accession Number:183581854
  • Copyright Statement:Copyright of International Journal of Modern Physics E: 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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