Machine learning assisted filtering approach for ion source optimization and control

The superconducting electron cyclotron resonance (ECR) ion source VENUS is the primary injector for the 88" Inch Cyclotron at LBNL and a copy of it is being installed as the primary driver for the FRIB linac. Optimization of these sources is made difficult by vast operation control spaces, a lack of useful models, and responses to control parameters that can be highly nonlinear. To guide source operation, we are employing Machine Learning tools and over a year of collected control and diagnostic data to produce local approximation functions that can be used to direct operators to control parameter changes that will maximize beam current while maintaining stability. We will describe a filtering approach that takes into account environmental conditions as well as the past and present state of the source to predict what change in the input has the highest probability of increasing the beam current and help guide the operator in the decision making process.