Periodic Reporting for period 4 - QGP tomography (A novel Quark-Gluon Plasma tomography tool: from jet quenching to exploring the extreme medium properties)
Periodo di rendicontazione: 2022-03-01 al 2023-08-31
QCD predicts the creation of Quark-Gluon Plasma (QGP) at extremely high energy densities. Composed of interacting quarks, antiquarks, and gluons, this state of matter is believed to have existed shortly after the Big Bang. Modern studies of QGP are conducted through ultra-relativistic heavy-ion collisions at facilities like RHIC at BNL and LHC at CERN.
The discovery of QGP in these experiments is now well-accepted, but understanding its properties remains challenging. Currently, QGP is viewed as a nearly perfect fluid, with its shear viscosity over entropy ratio (eta/s) approaching a conjectured universal lower bound. Interestingly, similar behavior is observed in ultracold Fermi gases, suggesting parallels between extremely hot and cold systems.
However, the portrayal of QGP as a nearly perfect fluid is under scrutiny. For most substances, eta/s reaches a minimum near the phase transition temperature (Tc), increasing with temperature rather than remaining constant. Studies, including hydrodynamics simulations, indicate that bulk medium simulations are not sensitive to substantial increases in eta/s near Tc, questioning the overly idealized perfect fluid notion.
Given that this perfect fluid picture emerges from low momentum data and hydrodynamic models, there's a need for independent datasets and theoretical predictions to refine our understanding of QGP. Our research proposes high-momentum (high-pt) parton data, compared with pQCD predictions, to explore QGP properties.
The discovery of QGP in these experiments is now well-accepted, but understanding its properties remains challenging. Currently, QGP is viewed as a nearly perfect fluid, with its shear viscosity over entropy ratio (eta/s) approaching a conjectured universal lower bound. Interestingly, similar behavior is observed in ultracold Fermi gases, suggesting parallels between extremely hot and cold systems.
However, the portrayal of QGP as a nearly perfect fluid is under scrutiny. For most substances, eta/s reaches a minimum near the phase transition temperature (Tc), increasing with temperature rather than remaining constant. Studies, including hydrodynamics simulations, indicate that bulk medium simulations are not sensitive to substantial increases in eta/s near Tc, questioning the overly idealized perfect fluid notion.
Given that this perfect fluid picture emerges from low momentum data and hydrodynamic models, there's a need for independent datasets and theoretical predictions to refine our understanding of QGP. Our research proposes high-momentum (high-pt) parton data, compared with pQCD predictions, to explore QGP properties.
Our approach posits that varying QGP parameters will result in different temperature profiles during QGP expansion. By analyzing high-pt partons, which experience varying temperatures and path lengths through the QGP, we can probe these temperature profiles. Differences in energy loss for light and heavy partons across a range of high-pt observables, when compared with experimental data, will indicate which temperature profiles and QGP properties align with high-pt data. This methodology underpins DREENA tomography tool that we developed in the project.
Before our project, there were two different relativistic heavy ion physics subfields - low-pt and high-pt. We demonstrated the synergy of joining them. A highly advanced numerical procedure (DREENA) had to be developed for this goal, which we successfully achieved. Significant risks were related to the project, as this was the first time that bulk QGP properties were constrained by low- and high-pt data. Within this framework, we tested the sensitivity of high-pt observables to different T profiles, showing that T profiles with the same predictions in the low-pt region lead to notably different predictions in the high-pt region. This result strongly supported our project idea that high-pt probes can be successfully used to infer the properties of QGP in the regions where low-pt theory and data cannot provide further constraints. Subsequently, we extensively used the DREENA framework to explore the bulk QGP properties, e.g. i) further constrain the early evolution and point to the late thermalization of QGP, 2) propose a novel observable to extract the anisotropy of QGP, 3) further constrain the eta/s T dependence, and point out that fluid dynamics and quasiparticle picture in QGP might not be inconsistent with each other, etc.
Before our project, there were two different relativistic heavy ion physics subfields - low-pt and high-pt. We demonstrated the synergy of joining them. A highly advanced numerical procedure (DREENA) had to be developed for this goal, which we successfully achieved. Significant risks were related to the project, as this was the first time that bulk QGP properties were constrained by low- and high-pt data. Within this framework, we tested the sensitivity of high-pt observables to different T profiles, showing that T profiles with the same predictions in the low-pt region lead to notably different predictions in the high-pt region. This result strongly supported our project idea that high-pt probes can be successfully used to infer the properties of QGP in the regions where low-pt theory and data cannot provide further constraints. Subsequently, we extensively used the DREENA framework to explore the bulk QGP properties, e.g. i) further constrain the early evolution and point to the late thermalization of QGP, 2) propose a novel observable to extract the anisotropy of QGP, 3) further constrain the eta/s T dependence, and point out that fluid dynamics and quasiparticle picture in QGP might not be inconsistent with each other, etc.
Finally, with the upcoming high-luminosity data from RHIC and LHC, the DREENA framework is poised to become an exceptional tool for extracting bulk QGP properties from these extensive data sets, thereby greatly enhancing the utility of both our framework and these landmark scientific investments. Additionally, the framework exhibits considerable promise for future integration with sophisticated computational methods, including machine learning and Bayesian analysis, further establishing its potential as a groundbreaking resource in studying QGP properties. This emerging research direction, which we are currently starting, has been enabled by the support of ERC funding.