The PUMA experiment at CERN utilizes antiprotons to investigate the surface structure of radioactive nuclei. Precise control of trapped charged-particle plasmas is essential for the efficient transport and cooling of antiprotons. To develop and optimize these techniques, the CAT trap at TU Darmstadt serves as a dedicated test and development platform for studies of electron plasmas and their manipulation.

In this Master thesis, the candidate will extend the existing EPICS-based control system of the CAT trap to enable automated optimization of experimental parameters using advanced algorithms (e.g. BOBYQA) and machine-learning approaches. The goal is to improve key plasma properties such as the number of trapped electrons, trapping efficiency, and plasma temperature, thereby providing the basis for reliable and reproducible plasma operation.

Furthermore, the student will contribute to the development of an improved cold-field-emission electron gun. The goal is to provide a reliable and controllable source of electrons, enabling reproducible plasma production and improved studies of plasma confinement and manipulation in the CAT trap.

Core data