3D Simulation studies of mixed plasma confinement at $ AE\bar{g}IS$

The $ AE\bar{g}IS$ (Antimatter Experiment: Gravity, Interferometry and Spectroscopy) project, based at CERN's Antiproton Decelerator (AD) facility seeks to probe the Weak Equivalence Principle (WEP) for antimatter. It has undergone significant enhancements, capitalizing on the increased quantity of colder antiprotons made available by the Extra Low Energy Antiproton Ring (ELENA) decelerator. These improvements aim to create a horizontal pulsed beam of antihydrogen atoms and enable a direct investigation into the impact of gravity. The $ AE\bar{g}IS$ experiment consists of a Penning Malmberg trap comprising cylindrical electrodes within a 5 T and a 1 T axial magnetic field region. The 5 T field captures cold antiprotons, while the 1 T field is used for further trapping which ultimately leads to antihydrogen production. To maximize the antihydrogen formation, it is crucial to have a detailed understanding of the properties of trapped antiprotons, which can be achieved by realistic 3D simulation studies for the dynamics of particle confinement. Previous studies indicated antiprotons exhibit greater stability in a shallower potential well \cite{rawat2024sim}. In this work, we examine the dynamics of antiprotons by varying the outer electrode potentials while maintaining constant potentials at the inner electrodes of the electrostatic trap, utilizing an Electrostatic Particle-In-Cell (ES-PIC) solver in the CST (Computer Simulation Technology) studio. We extended the studies on the temporal evolution of a mixed plasma generated with the introduction of electrons inside the trap along with the antiprotons by observing the effect of their properties: density, and temperature. Additionally, we provide an overview of the results obtained for the energy evolution of antiprotons using a Rotating Wall (RW) electrode at different RW frequencies. Finally, we summarize our plans to develop a full digital twin of the $ AE\bar{g}IS$ experiment for the next two years, providing valuable insights into the parameters required for optimized experiments