Periodic Reporting for period 3 - QGP tomography (A novel Quark-Gluon Plasma tomography tool: from jet quenching to exploring the extreme medium properties) Reporting period: 2020-09-01 to 2022-02-28 Summary of the context and overall objectives of the project At extremely high energy densities, QCD predicted the creation of a new form of matter, called Quark-Gluon Plasma (QGP). It is believed that QGP existed immediately after the Big-Bang, which marked the beginning of the universe. Today, QGP is explored through Little Bangs in ultra-relativistic heavy-ion collisions at landmark experiments, such as RHIC at BNL and LHC at CERN.It is by now widely accepted that QGP is discovered in RHIC and LHC experiments. However, it is now a challenge to understand its properties. According to the current paradigm, QGP is considered to be a nearly perfect fluid throughout its evolution. However, is this paradigm realistic? Note that for other substances eta/s (viscosity over entropy density ratio) has a minimum near phase transition temperature (Tc) and then increases with temperature (T). Moreover, it was shown by several studies that QGP medium simulations are insensitive to a large increase in eta/s not far away from Tc. These lead to the notion of the perfect fluid being too perfect, i.e. the fluid with very low viscosity throughout QGP evolution being unrealistic.Since the perfect fluid picture of QGP comes from low momentum (pt) data and hydrodynamic models, the question is how to provide a substantially different dataset and corresponding theoretical predictions, which may point to an improved QGP picture. We propose that this opportunity is provided by the data on the rare, high-momentum (high-pt) partons, through their comparison with pQCD predictions.Our main idea is that different QGP medium parameters will lead to different T profiles of the expanding QGP. Through high-pt partons, we can directly probe these various T profiles. That is, high-pt partons traversing QGP in various directions will sense different T dependences and path lengths. This will then lead to different energy losses, and consequently different predictions for both light and heavy partons, and a range of high-pt observables. Comparing these predictions with experimental data will then allow inferring which T profiles (and consequently which QGP properties) are consistent with the high-pt data. Importantly, the energy loss is larger for higher temperatures (with strong T dependence). We thus expect to have larger sensitivity for inferring QGP properties at higher T, which is in distinction to low momentum data, which are the least sensitive at high T. Therefore, high-pt theory and data will provide a powerful new constraint for inferring QGP properties. Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far To implement the idea outlined above, it is crucial to have a reliable high-pt parton energy loss model. With this goal in mind, over the past several years, we developed the state-of-the-art dynamical energy loss formalism that has several unique features necessary for the realistic description of high-momentum parton medium interactions. However, due to its complexity, the model did not take into account the medium evolution, so the first task of our project was to redevelop the formalism to include the medium evolution within the energy loss model. The temperature profiles (which are direct outputs of the bulk medium simulations) are now a direct input in our energy loss model, which is a major advantage for the project implementation.The redeveloped energy loss model was incorporated in a fully optimized numerical framework DREENA (Dynamical Radiative and Elastic ENergy loss Approach). DREENA is fully modular (i.e. it can include any temperature profile) and allows systematic comparison of experimental data and theoretical predictions, obtained by the same formalism and the same parameter set and with no fitting parameters used in model testing. In particular, the framework can efficiently and simultaneously generate predictions for:• Different observables (e.g. both RAA and v2)• Different collision systems (Pb+Pb, Au+Au, Xe+Xe, Cr+Cr, Ar+Ar, O+O)• Different probes (light and heavy)• Different collision energies and different centralitiesDuring the first project period, this developed framework enabled us to:• Propose a method (i.e. appropriate observable and appropriate systems) to differentiate between different energy loss models in QGP.• Analyze the sensitivity of high-pt probes to initial and final QGP stages, which are crucial for understanding QGP properties.• Propose the observable to directly infer the shape of the QGP droplet from the data, which presents the first application of high-pt data to infer the bulk QGP properties!• Demonstrate significant sensitivity of high-pt observables to different temperature profiles.The above results, obtained during the first project period, strongly support our project idea that high-pt probes are crucial for inferring the QGP properties Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far) During the first project period, our main achievement was the development of the DREENA framework, which combines full state-of-the-art high-pt dynamical energy loss with temperature profiles generated from the state of the art hydrodynamics or parton transport models. Two main advantages of the DREENA framework are that: i) it is based on a state-of-the-art dynamical energy loss formalism, which has key ingredients that are not available by other energy loss models, ii) it is fully modular, i.e. it can incorporate any temperature profile as a natural (i.e. direct) input in our model. Development of such a framework has been highly non-trivial, as our dynamical energy loss formalism is complex, where all its ingredients have to be kept for a reliable description of high-pt parton medium interactions. The framework is fully optimized, which enables efficiently generating computational predictions. In particular, in the upcoming project period, a very large number of temperature profiles will be tested to extract the bulk QGP properties.Consequently, our DREENA framework presents a core of our new tomography tool, which is schematically shown in the Figure below: We will generate temperature profiles by varying the bulk medium parameters in the range where they agree with the low pt data. We will further use these profiles as an input in DREENA to generate high-pt predictions for a wide range of light and heavy observables. Comparing the predictions with the data will allow us to select the QGP parameters that are in accordance with both low and high-pt data. We will further fine-tune the selected parameter range by repeating the procedure, where we will now vary the new parameters on a finer scale.This DREENA framework will help us in addressing several currently most pressing questions in the field:• Is QGP a fluid or a gas-like system?• Can QGP exist in small systems (or only in collisions of large ions)?• Can a single theoretical approach explain a wealth of experimental data?The project will also lead to the following significant, more general gains:• Develop a novel tool to put massive data produced at LHC and RHIC experiments to optimal use.• Possibly develop a more realistic picture of the exciting new form of matter.• The tool and its predictions will be ready for the down of the high precision era at RHIC and LHC.The project will therefore allow better exploiting these two landmark science investments and may allow addressing some of the most prominent questions on properties of this extreme form of matter. The scheme of QGP Tomography tool developed through our ERC project.
