Speaker
Description
Reliability, availability and maintainability determine whether or not a large-scale accelerator system can be operated in a sustainable, cost-effective manner. The operation of specific accelerator equipment and IT resources requires an increasingly higher focus on data analysis to meet these requirements. In addition setting up the machine for beam can be a time-consuming activity for the operators which can not always be optimised by standard algorithms because of high dimensional data and unclear correlations.
Existing optimiser and ML algorithms can be leveraged to produce models for these problems, based on historical data and/or reinforcement learning. In this presentation we present the fruit of internal discussions at CERN regarding ML applications, such as Linac4 transmission efficiency optimisation using a Powell optimisers and CNNs for classifying beam dump BTVDD images.
