Project C06: Flow, extreme magnetic fields, and data-driven analysis of hadronic collisions

Project Leaders: Andrea Dubla, Silvia Masciocchi, Ilya Selyuzhenkov

Summary: A detailed understanding of collective QCD dynamics as probed by high-energy heavy-ion collisions is the central goal of this project. Within the previous two funding periods, we have developed a new framework to solve fluid dynamics evolution equations with mode expansion (FLUIDuM) and a new efficient resonance decay package (FASTRESO). Together, they provide a versatile description of the heavy-ion collision evolution, which in the meantime became an asset in the community, and is used by all heavy-ion projects in the CRC. Further, we developed a Bayesian inference framework, for the very first time based on a neural network emulator and Markov-Chain Monte Carlo: it is used to compare our model with experimental data from heavy-ion collisions, and extract precise information on the hot QCD matter properties. We have also initiated investigations of the collective effects in small systems produced in electron-proton interactions, which triggered the proposal of the new ABC* project. For the third funding period, we want to build on these successful achievements and significantly extend our understanding of the hot QCD matter by including new dimensions to our model and tuning them with high-precision experimental data. We plan to work on three main objectives:

C06-1 Longitudinal description of the quark-gluon plasma.

While many insights on heavy-ion collision physics were gained from analysing azimuthally dependent perturbations, rapidity-dependent (longitudinal) perturba- tions are currently much less understood. As a result of the longitudinal expansion and of relativistic causality, correlations over a wide range in rapidity can only be generated at rather early times of the collision. Un- derstanding the longitudinal dynamics is also required by C06-2 for realistic modelling of the magnetic field effects, in particular charge dependent particle production coupled to the collective flow. Studying longitudinal flow fluctuations will therefore provide information on initial conditions, and on thermal and quantum fluctua- tions. The description provided by fluid dynamics models will be systematically compared with experimental data, applying the methods developed in C06-3. In close connection to this objective, we will also study the net baryon number transport, which is sensitive to the heat conductivity of the quark-gluon plasma (QGP). We will investigate models of the initial distribution of the net baryon number, in the transverse plane and as a function of rapidity. In order to perform these investigations and for systematic comparisons, we consider to use fluid dynamics frameworks such as MUSIC or Trajectum and an extension of the FLUIDuM framework to treat the system evolution in 3+1 dimensions. The transport coefficients used by the fluid dynamics models will be provided, whenever available, by the A02 project.

C06-2 Extreme magnetic fields in heavy-ion collisions.

Magnetic fields of about 1018 Gauss are created early in collisions of charged heavy nuclei. The time-dependent magnetic field and the expanding QCD matter induce charged currents due to the combination of Faraday and Hall effects. This gives access to transport properties of the QGP such as the electric conductivity, and can solve an outstanding puzzle: the strong CP problem in QCD. We aim at providing a consistent approach to solve numerically the fluid dynamics equations coupled to Maxwell’s equations in the three-dimensional description of the expanding QGP. Several experimental obser- vations are partially consistent with the effects of magnetic fields in heavy-ion collisions. However, background contributions, such as local charge conservation coupled with the anisotropic flow, prevent their unambiguous interpretation. We will investigate new observables sensitive to the strength and lifetime of the magnetic field, such as the charge dependence of the directed flow relative to the spectator plane, which is sensitive to the presence of electromagnetic fields and the QGP electric conductivity. The extension to a 3D+1 non-boost invariant fluid dynamic framework, which will be performed in C06-1, is essential to perform phenomenological studies of charge-dependent anisotropic flow. The tools developed in C06-3 will be crucial to reach conclusions about the influence of the magnetic fields.

C06-3 Assessing QCD parameters using data-driven analysis.

We intend to develop further AI methods to unravel the QCD matter properties encoded in the constantly increasing amounts of precise heavy-ion ex- perimental data. We will expand the Bayesian framework to scan new observables and model parameters, and apply it to the investigations in C06-1 and C06-2. We will deploy new algorithms to access QCD matter properties, such as invertible neural networks, to constrain, for example, the temperature dependence of the QGP transport coefficients using experimental data and to distinguish the different effects to the phenomena studied in C06-2. Unsupervised machine learning methods and the application of persistent homology will be explored, in order to extract persistent features encoded in real data.