DREENA-A framework as a QGP tomography tool

Abstract

QGP tomography aims to constrain the QGP parameters by exploiting both low and high-p⊥ theory and data. With this goal in mind, 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 adjust parameters within the energy loss model, allowing it to 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. The provided code allows researchers to use their own QGP evolution models to straightforwardly generate high-p⊥ predictions.