Author: Fol, E.
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MOPOST047 Determination of the Phase-Space Stability Border with Machine Learning Techniques 183
 
  • F.F. Van der Veken, R. Akbari, M.P. Bogaert, E. Fol, M. Giovannozzi, A.L. Lowyck, C.E. Montanari, W. Van Goethem
    CERN, Meyrin, Switzerland
 
  The dynamic aperture (DA) of a hadron accelerator is represented by the volume in phase space that exhibits bounded motion, where we disregard any disconnected parts that could be due to stable islands. To estimate DA in numerical simulations, it is customary to sample a set of initial conditions using a polar grid in the transverse planes, featuring a limited number of angles and using evenly distributed radial amplitudes. This method becomes very CPU intensive when detailed scans in 4D, and even more in higher dimensions, are used to compute the dynamic aperture. In this paper, a new method is presented, in which the border of the phase-space stable region is identified using a machine learning (ML) model. This allows one to optimise the computational time by taking the complex geometry of the phase space into account, using adaptive sampling to increase the density of initial conditions along the border of stability.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOST047  
About • Received ※ 06 June 2022 — Accepted ※ 17 June 2022 — Issue date ※ 20 June 2022  
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MOPOPT047 Experimental Demonstration of Machine Learning Application in LHC Optics Commissioning 359
 
  • E. Fol, F.S. Carlier, J. Dilly, M. Hofer, J. Keintzel, M. Le Garrec, E.H. Maclean, T.H.B. Persson, F. Soubelet, R. Tomás García, A. Wegscheider
    CERN, Meyrin, Switzerland
  • J.F. Cardona
    UNAL, Bogota D.C, Colombia
 
  Recently, we conducted successful studies on the suitability of machine learning (ML) methods for optics measurements and corrections, incorporating novel ML-based methods for local optics corrections and reconstruction of optics functions. After performing extensive verifications on simulations and past measurement data, the newly developed techniques became operational in the LHC commissioning 2022. We present the experimental results obtained with the ML-based methods and discuss future improvements. Besides, we also report on improving the Beam Position Monitor (BPM) diagnostics with the help of the anomaly detection technique capable to identify malfunctioning BPMs along with their possible fault causes.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT047  
About • Received ※ 07 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 06 July 2022  
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WEPOST008 Optics Correction Strategy for Run 3 of the LHC 1687
 
  • T.H.B. Persson, F.S. Carlier, A. Costa Ojeda, J. Dilly, V. Ferrentino, E. Fol, H. García Morales, M. Hofer, E.J. Høydalsvik, J. Keintzel, M. Le Garrec, E.H. Maclean, L. Malina, F. Soubelet, R. Tomás García, A. Wegscheider, L. van Riesen-Haupt
    CERN, Meyrin, Switzerland
  • J.F. Cardona
    UNAL, Bogota D.C, Colombia
 
  After almost 4 years of shutdown the LHC is again operational in 2022. Experience from the previous Long Shutdown (LS) has shown that the local errors around the triplet magnets changed significantly and it is likely we will again see different errors in 2022. In the LHC there is an interplay between the linear and the nonlinear correction which can make the corrections difficult and time-consuming to find. In this article, we describe the measurements and corrections performed during the commissioning in 2022 in order to control both the linear and the nonlinear optics to high precision.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOST008  
About • Received ※ 08 June 2022 — Revised ※ 25 June 2022 — Accepted ※ 04 July 2022 — Issue date ※ 10 July 2022
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WEPOMS046 Machine Learning-Based Modeling of Muon Beam Ionization Cooling 2354
 
  • E. Fol, D. Schulte
    CERN, Meyrin, Switzerland
  • C.T. Rogers
    STFC/RAL/ISIS, Chilton, Didcot, Oxon, United Kingdom
 
  Surrogate modeling can lead to significant improvements of beam dynamics simulations in terms of computational time and resources. Application of supervised machine learning, using collected simulation data allows to build surrogate models which can estimate beam parameters evolution based on the provided cooling channel design. The created models help to understand the correlations between different lattice components and the importance of specific beam properties for the cooling performance. We present the application of surrogate modeling to enhance final muon cooling design studies, demonstrating the potential of such approach to be integrated into the design and optimization of other components of future colliders.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOMS046  
About • Received ※ 07 June 2022 — Revised ※ 28 June 2022 — Accepted ※ 04 July 2022 — Issue date ※ 05 July 2022
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WEPOMS047 Automated Design and Optimization of the Final Cooling for a Muon Collider 2358
 
  • E. Fol, D. Schulte, B. Stechauner
    CERN, Meyrin, Switzerland
  • C.T. Rogers
    STFC/RAL/ISIS, Chilton, Didcot, Oxon, United Kingdom
  • J. Schieck
    HEPHY, Wien, Austria
 
  The desired beam emittance for a Muon collider is several orders of magnitude less than the one of the muon beams produced at the front-end target. Ionization cooling has been demonstrated as a suitable technique for the reduction of the muon beam emittance. Final cooling, as one of the most critical stages of the muon collider complex, necessitates careful design and optimization in order to control the beam dynamics and ensure efficient emittance reduction. We present an optimization framework based on ICool simulation code and application of different optimization algorithms, to automatize the choice of optimal initial muon beam parameters and simultaneous tuning of numerous final cooling components.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOMS047  
About • Received ※ 07 June 2022 — Revised ※ 14 June 2022 — Accepted ※ 23 June 2022 — Issue date ※ 03 July 2022
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