Author: Trad, G.
Paper Title Page
MOPOPT040 Summary of the Post-Long Shutdown 2 LHC Hardware Commissioning Campaign 335
 
  • A. Apollonio, O.Ø. Andreassen, A. Antoine, T. Argyropoulos, M.C. Bastos, M. Bednarek, B. Bordini, K. Brodzinski, A. Calia, Z. Charifoulline, G.-J. Coelingh, G. D’Angelo, D. Delikaris, R. Denz, L. Fiscarelli, V. Froidbise, M.A. Galilée, J.C. Garnier, R. Gorbonosov, P. Hagen, M. Hostettler, D. Jacquet, S. Le Naour, D. Mirarchi, V. Montabonnet, B.I. Panev, T.H.B. Persson, T. Podzorny, M. Pojer, E. Ravaioli, F. Rodriguez-Mateos, A.P. Siemko, M. Solfaroli, J. Spasic, A. Stanisz, J. Steckert, R. Steerenberg, S. Sudak, H. Thiesen, E. Todesco, G. Trad, J.A. Uythoven, S. Uznanski, A.P. Verweij, J. Wenninger, G.P. Willering, D. Wollmann, S. Yammine
    CERN, Meyrin, Switzerland
  • V. Vizziello
    INFN/LASA, Segrate (MI), Italy
 
  In this contribution we provide a summary of the LHC hardware commissioning campaign following the second CERN Long Shutdown (LS2), initially targeting the nominal LHC energy of 7 TeV. A summary of the test procedures and tools used for testing the LHC superconducting circuits is given, together with statistics on the successful test execution. The paper then focuses on the experience and observations during the main dipole training campaign, describing the encountered problems, the related analysis and mitigation measures, ultimately leading to the decision to reduce the energy target to 6.8 TeV. The re-commissioning of two powering sectors, following the identified problems, is discussed in detail. The paper concludes with an outlook to the future hardware commissioning campaigns, discussing the lessons learnt and possible strategies moving forward.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT040  
About • Received ※ 08 June 2022 — Revised ※ 13 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 27 June 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
MOPOPT041 Artificial Intelligence-Assisted Beam Distribution Imaging Using a Single Multimode Fiber at CERN 339
 
  • G. Trad, S. Burger
    CERN, Meyrin, Switzerland
 
  In the framework of developing radiation tolerant imaging detectors for transverse beam diagnostics, the use of machine learning powered imaging using optical fibers is explored for the first time at CERN. This paper presents the pioneering work of using neural networks to reconstruct the scintillating screen beam image transported from a harsh radioactive environment over a single, large-core, multimode, optical fiber. Profiting from generative modeling used in image-to-image translation, conditional adversarial networks have been trained to translate the output plane of the fiber, imaged on a CMOS camera, into the beam image imprinted on the scintillating screen. Theoretical aspects, covering the development of the dataset via geometric optics simulations, modeling the image propagation in a simplified model of an optical fiber, and its use for training the network are discussed. Finally, the experimental setups, both in the laboratory and at the CLEAR facility at CERN, used to validate the technique and evaluate its potential are highlighted.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT041  
About • Received ※ 08 June 2022 — Revised ※ 14 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 19 June 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)