Author: Garcia-Tabares, A.
Paper Title Page
WEPAF062 Machine Learning Methods for Optics Measurements and Corrections at LHC 1967
  • E. Fol, F.S. Carlier, J.M. Coello de Portugal, A. Garcia-Tabares, R. Tomás
    CERN, Geneva, Switzerland
  The application of machine learning methods and concepts of artificial intelligence can be found in various industry and scientific branches. In Accelerator Physics the machine learning approach has not found a wide application yet. This paper is devoted to evaluation of machine learning methods aiming to improve the optics measurements and corrections at LHC. The main subjects of the study are devoted to recognition and analysis of faulty beam position monitors and prediction of quadrupole errors using clustering algorithms, decision trees and artificial neural networks. The results presented in this paper clearly show the suitability of machine learning methods for the optics control at LHC and the potential for further investigation on appropriate approaches.  
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