Although machine learning techniques have been applied to particle accelerators since the late 1980s, a renaissance has only been seen in recent years. This is due, in part, to the success of modern developments such as deep learning and, in part, is a result of the sophistication and data-intensiveness of current machines. The system dynamics of particle accelerators tend to involve large parameter spaces which evolve over multiple time scales, and interrelations between accelerator subsystems may be complex and nonlinear.

As a result, there is growing interest from the particle accelerator community to use machine learning techniques to analyse large quantities of archived data to accurately model accelerator systems, detect anomalous machine behaviour, and perform active tuning and control. It is expected that machine learning will become an increasingly valuable tool to meet new demands for beam energy, brightness, reliability, and stability.

This seminar will review the ongoing research activities in this area, as well as the contribution of the University of Malta in collaboration with the particle accelerator community.

Attendees are kindly asked to register online. 

The Data Science Research Platform (DSRP) at the University of Malta conducts research in the interdisciplinary field of data science. The scope of the group is to use signal processing, machine learning and statistics to develop innovative techniques and to extract useful knowledge from various data sources in an effective manner to benefit the wider public. 

For more information about the DSRP, please visit the dedicated website. 

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