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Nuclear Theory·nucl-th

Friday·August 16, 2024

5 papers—3 primary·2 cross-listed

All5Primary3Cross-lists2
  1. 04

    [Submitted on 15 Aug 2024] (cross-list from hep-ph)

    Collision Energy Dependence of Particle Ratios and Freeze-out Parameters in Ultra Relativistic Nucleus Nucleus Collisions

    Iqbal Mohi Ud Din (1)🇮🇳 · Sameer Ahmad Mir (1)🇮🇳 · Nasir Ahmad Rather (1)🇮🇳 · Saeed Uddin (1)🇮🇳 · Rameez Ahmad Parra (2) ((1) Jamia Millia Islamia (2) University of Kashmir)🇮🇳

    This work investigates the thermo-chemical freeze-out condition of the multi-component hot and dense hadron resonance gas (HRG) formed in the ultra-relativistic nucleus-nucleus collisions (URNNC). The van der Waals (VDW) type model used in the present analysis incorporates the repulsive as well as attractive interactions among the hadrons. The baryons (antibaryons) are treated as incompressible objects. Using this theoretical approach the values of the model freeze-out parameters of the system are extracted over a wide range of collision energy by analyzing experimental data on like-mass antibaryon to baryon ratios. The same set of parameters is found to explain the energy dependence of several other particle ratios quite satisfactorily. We find that the horn-like structures seen in the ratios of strange particles to pions as a function of the collision energy cannot be explained by the VDW-HRG model alone without considering the strangeness imbalance effect in the system. We have compared our freeze-out line with those obtained earlier. The correlation between the and ratios is also examined.

    Comments:
    15 pages, 10 Figures
    Subjects:
    High Energy Physics — Phenomenology (hep-ph); Nuclear Theory (nucl-th)
    arXiv:
    2408.07943 [pdf]
    NPA(2025)·9 citations
  2. 05

    [Submitted on 15 Aug 2024] (cross-list from hep-ph)

    Bayesian Inference analysis of jet quenching using inclusive jet and hadron suppression measurements

    R. Ehlers🇺🇸 · Y. Chen🇺🇸 · J. Mulligan🇺🇸 · Y. Ji🇺🇸 · A. Kumar🇨🇦 · S. Mak🇺🇸 · P. M. Jacobs🇺🇸 · A. Majumder🇺🇸 · A. Angerami🇺🇸 · R. Arora🇺🇸 · S. A. Bass🇺🇸 · R. Datta🇺🇸 and 41 other authors

    The JETSCAPE Collaboration reports a new determination of the jet transport parameter in the Quark-Gluon Plasma (QGP) using Bayesian Inference, incorporating all available inclusive hadron and jet yield suppression data measured in heavy-ion collisions at RHIC and the LHC. This multi-observable analysis extends the previously published JETSCAPE Bayesian Inference determination of , which was based solely on a selection of inclusive hadron suppression data. JETSCAPE is a modular framework incorporating detailed dynamical models of QGP formation and evolution, and jet propagation and interaction in the QGP. Virtuality-dependent partonic energy loss in the QGP is modeled as a thermalized weakly-coupled plasma, with parameters determined from Bayesian calibration using soft-sector observables. This Bayesian calibration of utilizes Active Learning, a machine--learning approach, for efficient exploitation of computing resources. The experimental data included in this analysis span a broad range in collision energy and centrality, and in transverse momentum. In order to explore the systematic dependence of the extracted parameter posterior distributions, several different calibrations are reported, based on combined jet and hadron data; on jet or hadron data separately; and on restricted kinematic or centrality ranges of the jet and hadron data. Tension is observed in comparison of these variations, providing new insights into the physics of jet transport in the QGP and its theoretical formulation.

    Comments:
    20 pages, 10 figures, 2 tables, submitted to PRC; updated acknowledgements
    Subjects:
    High Energy Physics — Phenomenology (hep-ph); Nuclear Experiment (nucl-ex); Nuclear Theory (nucl-th)
    arXiv:
    2408.08247 [pdf]
    PRC(2025)·56 citations

Affiliations

first authorsco-authorsvia INSPIRE