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Overview of the Machine Learning and Numerical Optimiser Applications on Beam Transfer Systems for LHC and Its Injectors |
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- F.M. Velotti, M.J. Barnes, E. Carlier, Y. Dutheil, M.A. Fraser, B. Goddard, N. Magnin, R.L. Ramjiawan, E. Renner, P. Van Trappen
CERN, Meyrin, Switzerland
- E. Waagaard
Uppsala University, Uppsala, Sweden
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Machine learning and numerical optimisation algorithms are getting more and more popular in the accelerator physics community and, thanks to the computing power available, their application in daily operation more likely. In the CERN accelerator complex, and specifically on the beam transfer systems, many promising exploitation of these numerical tools have been put in place in the last years. Some of the state-of-the-art machine learning models have been explored and used to solve problems that were never fully addressed in the past. In this paper, the most recent results of application of machine learning and numerical optimisation for injection, extraction and transfer of beam from machine and to experimental areas are presented. An overview of the possible next steps and shortcomings is finally discussed.
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