Speaker
Description
Through years of HEP core investment and the AI-Hardware project, we have established a machine-learning + low-level RF (ML+LLRF) foundation for real-time control of lasers and accelerator subsystems. The foundation is built on the open, reconfigurable LBNL Marble platform and a set of general-purpose DSP repositories designed to be portable across plant classes, rather than tied to a single application. This substrate already underpins two demonstrated capabilities: ML-based laser combining and laser-pointing stabilization at BELLA. We are now applying the same approach to PIP-II SRF resonance control—a large collaboration with FNAL, SLAC, ANL, MSU, JLab and others—which shows strong promise in simulation and is slated for hardware demonstration, with early hardware tests at the Vertical Test Stand (VTS) showing promising results. Because the platform and DSP libraries are broadly reusable, they open a wide range of additional applications and test opportunities across facilities. This talk provides a status update on our demonstrated results, work in progress, and near-term plans, and outlines where an open, ML-ready control substrate can serve the broader community.
