Particle accelerators are huge, complex machines. Scientists and engineers have designed and built a novel machine learning system to use with the Continuous Electron Beam Accelerator Facility (CEBAF). The system monitors structures called accelerator cavities inside the particle accelerator. These cavities impart energy to beams of electrons for exploring the nucleus of the atom. Problems in these cavities can cause the CEBAF to trip off like a fuse. In its first field test, the machine learning system correctly identified which of these cavities were tripping off about 85 percent of the time. About 78 percent of the time, the system also correctly identified what kind of fault caused each cavity to trip.
CEBAF is the world’s primary research facility for exploring the nature of matter inside the atom’s nucleus. More than 1,650 nuclear physicists compete for limited research time to conduct experiments with CEBAF. Being able to identify potential problems in the machine early and quickly allows CEBAF operators to optimize the time available for experiments. The new machine learning system allows operators to identify the sources and types of problems nearly instantaneously. By quickly identifying problems, machine operators can resolve those problems faster. This reduces downtime and increases how much time experiments have on CEBAF and how much data they can collect. It also frees up time for machine experts to focus on other issues.