Conveners
Machine Learning & Reliability
- Rossano Giachino (CERN)
Description
Machine Learning has already been applied in some instances to particle accelerators, the recent advances (e.g. deep learning) and heightened interest in the community indicate that it will be an increasingly valuable tool to meet new demands for beam energy, brightness and stability.
The intent of this session is to introduce how problems in accelerator science and operation (in particular accelerator reliability) can be approached using ML techniques, and how these have the potential to provide benefits over classical approaches.
Enormous efforts are expended creating high-fidelity simulations of accelerator beamlines. While these simulations provide an initial starting point for operators, there exists a gap between the ideal simulated entity and the real-world implementation. Bridging that gap requires a brute force and time consuming task known as beam tuning. This project develops a data-driven approach to beam...
With the many advances in machine learning in recent years, adopting this technology for accelerator operation offers promising perspectives. At CRYRING@ESR we implemented an operator-grade machine automation application with beam optimization support based on a Genetic Algorithm. This tool was used for optimizing the beam intensity via several machine sub-systems such as injection, ion...
The deployment of Machine Learning (ML) applications in a production environment requires verification, validation, assurance, and trust. ML models are notoriously difficult to maintain in these environments where data and systems may evolve over time and long-term maintenance is required. The models require active management for (1) reproduction or replication of model weights, (2) monitoring...
In order to improve the day-to-day particle accelerators operations and maximize the delivery of the science, new analytical techniques are being explored for anomaly detection, classification, and prognostications. We describe the application of an uncertainty aware machine learning (ML) method using Siamese neural network model to predict upcoming errant beam pulses as Spallation Neutron...
