The PUMA experiment at CERN utilizes antiprotons to investigate the surface structure of radioactive nuclei. Since transporting these antiprotons is critical, precise control of the antiproton plasma is essential, and a dedicated test trap is operated at TU Darmstadt.

In this Bachelor thesis, the candidate improves the control system of the PUMA test trap by setting up a new control system based on EPICS. This enables the automatic optimization of trap quantities using algorithms (e.g. BOBYQA) or machine learning. In a second step, this will be used to optimize the number of trapped electrons, while also achieving a high trapping efficiency. Furthermore, it is planned to enable an automized control of the plasma temperature.