Periodic Reporting for period 2 - QGP tomography (A novel Quark-Gluon Plasma tomography tool: from jet quenching to exploring the extreme medium properties)
Reporting period: 2019-03-01 to 2020-08-31
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.
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 centralities
During 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
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.