Speaker
Description
Modern machine learning is becoming more widely applied to the field of particle accelerators. One such type of application is a virtual diagnostic (VD), where one reconstructs the output of time-consuming or destructive diagnostics using machine learning methods. In this contribution we present the application of a general structure of artificial neural networks (ANN) and training procedures which has been used to construct VDs for multiple different facilities. We have focused on an application of VDs which allows for online extraction of the beam's longitudinal phase space (LPS), otherwise measured destructively with transverse deflecting structures. We present how specific architectures of ANNs were chosen to produce accurate LPS predictions, their advantages and disadvantages. We report results from implementing these methods at the particle accelerators MAX IV in Sweden, the FERMI FEL in Italy and the SwissFEL at PSI in Switzerland. We show how these systems can be used to reach reliable predictions of the LPS for all three facilities. For future work, we show how virtual diagnostics could be further developed to suit the specific needs of operations at each facility.
| Broad physics domain | Accelerator Physics |
|---|---|
| AI/ML technique(s) to be presented | Supervised learning of combined dense and convolutional neural networks for image-based regression. |
