[Paper Review] DREENA-A framework as a QGP tomography tool

Dusan Zigic, Igor Salom+3 more|arXiv (Cornell University)|Oct 4, 2021

Particle Detector Development and Performance4 citations

TL;DR

DREENA-A is a fully optimized, parameter-free framework for Quark-Gluon Plasma (QGP) tomography that uses dynamical energy loss formalism to predict high-transverse-momentum observables (RAA and v₂) across light and heavy flavors. It uniquely exploits arbitrary temperature profiles from bulk medium simulations to sensitively distinguish QGP evolution scenarios via observable differences, enabling precision constraints on QGP properties at RHIC and LHC energies.

ABSTRACT

We present a fully optimised framework DREENA-A based on a state-of-the-art energy loss model. The framework can include any, in principle arbitrary, temperature profile within the dynamical energy loss formalism. Thus, 'DREENA' stands for Dynamical Radiative and Elastic ENergy loss Approach, while 'A' stands for Adaptive. DREENA-A does not use fitting parameters within the energy loss model, allowing it to fully exploit differences in temperature profiles which are the only input in the framework. The framework applies to light and heavy flavor observables, different collision energies, and large and smaller systems. This, together with the ability to systematically compare data and predictions within the same formalism and parameter set, makes DREENA-A a unique multipurpose QGP tomography tool.

Motivation & Objective

• To develop a unified, computationally efficient framework for QGP tomography that integrates realistic medium evolution into high-pT parton energy loss.
• To eliminate fitting parameters in energy loss modeling, ensuring predictions depend solely on input temperature profiles and fundamental QCD parameters.
• To enable systematic, multi-flavor, multi-energy comparison of theoretical predictions with experimental data across large and small collision systems.
• To test the sensitivity of high-pT observables (RAA and v₂) to different QGP temperature evolution scenarios, providing constraints on medium properties.
• To establish a robust, adaptive tool for constraining QGP bulk dynamics using both light and heavy flavor data in a consistent theoretical framework.

Proposed method

• The framework implements the dynamical energy loss formalism based on finite-temperature field theory, incorporating both radiative and collisional energy loss in a single theoretical framework.
• It uses a state-of-the-art energy loss model with no fitting parameters, relying on standard literature values for transport coefficients and screening masses.
• The framework accepts arbitrary temperature profiles as input, allowing it to model diverse QGP evolution scenarios (e.g., Glauber, T R ENTo, EKRT) without simplification.
• Numerical implementation is optimized for speed and convergence, enabling efficient generation of predictions across multiple collision systems and energies.
• Predictions are computed via pQCD convolution for parton energy loss, with final-state observables (RAA and v₂) calculated for charged hadrons, D, and B mesons.
• The framework is validated by comparing results with earlier versions (DREENA-C, DREENA-B) and with experimental data from CMS, ALICE, ATLAS, PHENIX, and STAR.

Figure 1: D meson $R_{AA}$ (left) and $v_{2}$ (middle) at 30-40 $\%$ centrality computed using different numbers of randomly generated trajectories (Monte Carlo approach), together with their deviations (right, scaled 1-norm was used as a metric) from the results averaged over the same ensemble of t

Experimental results

Research questions

• RQ1 Can high-pT observables (RAA and v₂) be used to distinguish between different QGP temperature evolution profiles in heavy-ion collisions?
• RQ2 How sensitive are D meson and B meson suppression patterns to variations in the QGP temperature profile compared to charged hadrons?
• RQ3 To what extent can the DREENA-A framework differentiate between QGP evolution models (e.g., Glauber vs. T R ENTo vs. EKRT) using only high-pT data?
• RQ4 Can the absence of fitting parameters in the energy loss model enhance the reliability of QGP tomography by isolating the impact of medium temperature evolution?
• RQ5 How do the predictions of DREENA-A compare with experimental data across different collision energies and centrality classes at RHIC and LHC?

Key findings

• DREENA-A successfully reproduces the expected hierarchy in RAA and v₂ across different temperature profiles, with EKRT (highest temperature) yielding the smallest RAA and T R ENTo (lowest anisotropy) yielding the smallest v₂.
• The framework shows clear, visually distinct differences in RAA and v₂ predictions for charged hadrons, D mesons, and B mesons across all three temperature evolution models at both RHIC and LHC energies.
• The sensitivity of high-pT observables to temperature profile variations is robust across all flavors and collision systems, confirming their utility as independent probes of QGP evolution.
• The framework’s predictions for RAA and v₂ in 200 GeV Au+Au collisions at 20-30% centrality are in good agreement with experimental data from PHENIX, STAR, ALICE, and CMS.
• The absence of fitting parameters ensures that observed differences in predictions are due solely to variations in the input temperature profiles, validating the framework’s role in model-independent QGP tomography.
• The DREENA-A framework enables efficient, repeatable, and consistent comparison of theoretical predictions with data across multiple observables and systems, making it a versatile tool for constraining QGP medium properties.

Figure 2: D meson $R_{AA}$ (left) and $v_{2}$ (middle) at 30-40 $\%$ centrality computed using different numbers of trajectories originating from equidistant points. Results are labeled by numbers $n_{\phi}\times(n_{x}\times n_{y})$ : jet directions are along $n_{\phi}$ uniformly distributed angles