Turbulence, magnetic reconnection, shocks, and particle acceleration: nonlinear processes in space, laboratory, and astrophysical plasmas

Space, laboratory, and astrophysical plasmas are seemingly different environments, which however host very similar processes: among them, turbulence, magnetic reconnection, and shocks, which all result in particle acceleration. These processes are highly non-linear, and closely interlinked. On the one hand, the turbulence cascade favors the onset of magnetic reconnection between magnetic islands and, on the other hand, magnetic reconnection can trigger turbulence in the reconnection outflows and separatrices. Similarly, shocks may form in collisional and collisionless reconnection processes and can be responsible for turbulence formation, as for instance in the turbulent magnetosheath. We are now in a fortunate time when the investigation of these processes based on simulations and observations are converging: simulations can deliver output which is approaching, in temporal and spatial scales, and in the coexistence of several scales, the complexity of an increasing number of the processes of interest. On the observation side, high cadence measurements of particles and fields, high resolution 3D measurements of particle distribution functions and multipoint measurements make it easier to reconstruct the 3D space surrounding the spacecrafts. The ever growing amount of data that both simulations and observations produce can be then combed through and organized with Artificial Intelligence and Machine Learning methods. This session welcomes simulations, observational, and theoretical works relevant for the study of the above mentioned plasma processes. Particularly welcome this year will be works focusing on the common aspects of turbulence, reconnection, and shocks in space, laboratory, and astrophysical plasmas. We also encourage papers proposing new methods, especially those rooted in Artificial Intelligence and Machine Learning, to extract new knowledge from these big observational and simulated data sets.