Electron beam control in the storage ring of the ELETTRA 2.0 accelerator using machine learning

In recent years, the advancement of electron synchrotron light sources has been crucial for synchrotron radiation users. Improving electron beam qualities, such as stability and intensity, has been key to generating high-brightness photon beams. To achieve a brighter photon beam, electron synchrotron accelerators have been undergoing significant design changes. A critical aspect of their design is precise control of particle trajectories and correction of errors to achieve a stable electron beam and, consequently, a high-intensity photon beam. One interesting approach in this field is the use of neural networks to correct the electron beam position in the storage ring, a process known as beam control. In this study, a convolutional neural network model has been developed for the first time to control the ELETTRA 2.0 storage ring, a synchrotron light source in Italy. The performance of this model, based on machine learning techniques, is approximately 6% better than the ISVD method, while also demonstrating high robustness to real-world data.