Conveners
Machine Learning & Artificial Intelligence & Automatic Prediction of Failures: Chair: Rossano Giachino (CERN)
- rossano giachino (CERN)
Description
Chair: Rossano Giachino (CERN)
The traditional method for determining the synchronous phase (SP) of beam typically relies on “phase scan method”. Despite its high precision and reliability, this method requires a significant amount of runtime. Processes such as phase drift caused by environmental disturbances or rapid recovery after cavity faults (such as Quench) necessitate repeated execution of the phase scan procedure....
Over the last few years several machine learning projects at Jefferson Lab have had a common focus to optimize operation of superconducting RF (SRF) cavities in the Continuous Electron Beam Accelerator Facility (CEBAF). Recent access to information-rich data has enabled this multi-faceted approach, which aims to (1) identify and classify types of faults from cavities, (2) extend the work to...
In the past years, we have witnessed an exponential growth of machine learning applications in practically any industry and any aspect of our daily lives. Particle accelerators are no exception. Many references of using Artificial Neural Networks (ANN) in particle accelerators can be found in the literature. Robotics, industrial controls, accelerator operations or beam optimizations are some...
