Speaker
Description
A dedicated experimental search for a muon electric dipole moment (EDM) is being set up at Paul Scherrer Institute. This experiment will search for a muon EDM signal with a final precision of \SI{6e-23}{e \cdot cm} using the frozen-spin technique, improving the current experimental limit by 3 orders of magnitude. To achieve the precision objective, it is important to optimize the setup to ensure maximum efficiency for recording an experimental signal. However, the optimization procedure is computationally expensive owing to complex dynamics of charged particles in electromagnetic fields, conflicting objectives and a multi-dimensional input space. Thus, we employ a genetic algorithm based on surrogate models to gain orders of magnitude in computational speed. In this talk, we present an overview of this optimization study based on simulations and discuss various ways of constructing surrogate models aided by machine learning techniques.
