Author: Fol, E.
Paper Title Page
MOPAB184 Unsupervised Learning Techniques for Tune Cleaning Measurement 624
 
  • H. Garcia Morales
    Oxford University, Physics Department, Oxford, Oxon, United Kingdom
  • E. Fol, R. Tomás García
    CERN, Geneva, Switzerland
 
  Precise measurements of tune and its stability are crucial for various optics analyses in the LHC, e.g. for the determination of the beta star using K-modulation. LHC BBQ system provides tune measurements online and stores the tune data. We apply unsupervised machine learning techniques on BBQ tune data in order to provide an automatic outlier detection method for better measurements of tune shifts and unexpected tune jitters.  
poster icon Poster MOPAB184 [0.354 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB184  
About • paper received ※ 14 May 2021       paper accepted ※ 09 June 2021       issue date ※ 11 August 2021  
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WEPAB026 Optics Measurements and Correction Plans for the HL-LHC 2656
 
  • T.H.B. Persson, X. Buffat, F.S. Carlier, R. De Maria, J. Dilly, E. Fol, D. Gamba, H. Garcia Morales, A. García-Tabarés Valdivieso, M. Giovannozzi, M. Hofer, E.J. Høydalsvik, J. Keintzel, M. Le Garrec, E.H. Maclean, L. Malina, P.K. Skowroński, F. Soubelet, R. Tomás García, F.F. Van der Veken, A. Wegscheider, D.W. Wolf, L. van Riesen-Haupt
    CERN, Meyrin, Switzerland
  • J.M. Coello de Portugal
    PSI, Villigen PSI, Switzerland
 
  The High Luminosity LHC (HL-LHC) will require stringent optics correction to operate safely and deliver the design luminosity to the experiments. In order to achieve this, several new methods for optics correction have been developed. In this article, we outline some of these methods and we describe the envisioned strategy of how to use them in order to reach the challenging requirements of the HL-LHC physics program.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB026  
About • paper received ※ 17 May 2021       paper accepted ※ 27 July 2021       issue date ※ 30 August 2021  
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WEPAB027 Optics Correction Strategy for Run 3 of the LHC 2660
 
  • T.H.B. Persson, R. De Maria, J. Dilly, E. Fol, H. Garcia Morales, M. Hofer, E.J. Høydalsvik, J. Keintzel, M. Le Garrec, E.H. Maclean, L. Malina, F. Soubelet, R. Tomás García, A. Wegscheider, D.W. Wolf, L. van Riesen-Haupt
    CERN, Meyrin, Switzerland
  • J.F. Cardona
    UNAL, Bogota D.C, Colombia
 
  The Run 3 of the LHC will continue to provide new challenges for optics corrections. In order to succeed and go beyond what was achieved previously, several new methods to measure and correct the optics have been developed. In this article we describe these methods and outline the plans for the optics commissioning in 2022.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB027  
About • paper received ※ 17 May 2021       paper accepted ※ 12 July 2021       issue date ※ 11 August 2021  
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THPAB055 Reconstruction of Linear Optics Observables Using Supervised Learning 3875
 
  • E. Fol, H. Garcia, R. Tomás García
    CERN, Geneva, Switzerland
 
  In the LHC, most of the optical functions can be obtained from turn-by-turn beam centroid data. However, the measurement of such observables as β* and the dispersion function require special dedicated techniques and additional operational time. In this work, we propose an alternative approach to estimate these observables using supervised machine learning, in case the dedicated measurements are not available but turn-by-turn data are. The performance of developed estimators is demonstrated on LHC simulations. Comparison to traditional techniques for the computation of beta-function will be also provided.  
poster icon Poster THPAB055 [0.713 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB055  
About • paper received ※ 19 May 2021       paper accepted ※ 19 July 2021       issue date ※ 15 August 2021  
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THPAB068 Denoising of Optics Measurements Using Autoencoder Neural Networks 3915
 
  • E. Fol, R. Tomás García
    CERN, Geneva, Switzerland
 
  Noise artefacts can appear in optics measurements data due to instrumentation imperfections or uncertainties in the applied analysis methods. A special type of semi-supervised neural networks, autoencoders, are widely applied to denoising tasks in image and signal processing as well as to generative modeling. Recently, an autoencoder-based approach for denoising and reconstruction of missing data has been developed to improve the quality of phase measurements obtained from harmonic analysis of LHC turn-by-turn data. We present the results achieved on simulations demonstrating the potential of the new method and discuss the effect of the noise in light of optics corrections computed from the cleaned data.  
poster icon Poster THPAB068 [0.881 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB068  
About • paper received ※ 19 May 2021       paper accepted ※ 13 July 2021       issue date ※ 02 September 2021  
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THPAB168 Optics Measurement by Excitation of Betatron Oscillations in the CERN PSB 4078
 
  • E.H. Maclean, F. Antoniou, F. Asvesta, H. Bartosik, C. Bracco, J. Dilly, E. Fol, H. Garcia Morales, M. Hofer, J. Keintzel, M. Le Garrec, T.E. Levens, L. Malina, T.H.B. Persson, T. Prebibaj, E. Renner, P.K. Skowroński, F. Soubelet, R. Tomás García, A. Wegscheider, L. van Riesen-Haupt
    CERN, Meyrin, Switzerland
 
  Optics measurement from analysis of turn-by-turn BPM data of betatron oscillations excited with a kicker magnet has been employed very successfully in many machines but faces particular challenges in the CERN PSB where BPM to BPM phase advances are sub-optimal for optics reconstruction. Experience using turn-by-turn oscillation data for linear optics measurements during PSB commissioning in2021 is presented, with implications for the prospect of such techniques in the PSB more generally.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB168  
About • paper received ※ 19 May 2021       paper accepted ※ 14 July 2021       issue date ※ 27 August 2021  
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