Author: Coello de Portugal, J.M.
Paper Title Page
WEPGW081 Unsupervised Machine Learning for Detection of Faulty Beam Position Monitors 2668
SUSPFO097   use link to see paper's listing under its alternate paper code  
 
  • E. Fol, J.M. Coello de Portugal, R. Tomás
    CERN, Geneva, Switzerland
 
  Unsupervised learning includes anomaly detection techniques that are suitable for the detection of unusual events such as instrumentation faults in particle accelerators. In this work we present the application of decision trees-based algorithm to faulty BPMs detection at the LHC. This method achieves significant improvements in quality of optics measurements and allows to identify relevant signal properties that contribute to fault detection.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-WEPGW081  
About • paper received ※ 14 May 2019       paper accepted ※ 21 May 2019       issue date ※ 21 June 2019  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
THPRB077 Optics Corrections Using Machine Learning in the LHC 3990
 
  • E. Fol, J.M. Coello de Portugal, R. Tomás
    CERN, Geneva, Switzerland
  • G. Franchetti
    GSI, Darmstadt, Germany
 
  Optics corrections in the LHC are based on a response matrix approach between available correctors and observables. Supervised learning has been applied to quadrupole error prediction at the LHC giving promising results in simulations and surpassing the performance of the traditional approach. A comparison of different algorithms is given and it is followed by the presentation of further possible concepts to obtain optics corrections using machine learning.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-THPRB077  
About • paper received ※ 14 May 2019       paper accepted ※ 21 May 2019       issue date ※ 21 June 2019  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)