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Title Fortune Telling or Physics Prediction? Deep Learning for On-Line Kicker Temperature Forecasting
  • F.M. Velotti, M.J. Barnes, B. Goddard, I. Revuelta
    CERN, Meyrin, Switzerland
Abstract The injection kicker system MKP of the Super Proton Synchrotron SPS at CERN is composed of 4 kicker tanks. The MKP-L tank provides additional kick needed to inject 26 GeV Large Hadron Collider LHC 25 ns type beams. This device has been a limiting factor for operation with high intensity, due to the magnet’s broadband beam coupling impedance and consequent beam induced heating. To optimise the usage of the SPS and avoid idle (kicker cooling) time, studies were conducted to develop a recurrent deep learning model that could predict the measured temperature evolution of the MKP-L, using the beam conditions and temperature history as input. In a second stage, the ferrite temperature is also estimated putting together the external temperature predictions from accurate thermo-mechanical simulations of the kicker magnet. In this paper, the methodology is described and details of the neural network architecture used, together with the implementation of an ad-hoc loss function, are given. The results applied to the SPS 2021 operational data are presented.
Paper download TUPOST044.PDF [0.339 MB / 4 pages]
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Conference IPAC2022
Series International Particle Accelerator Conference (13th)
Location Bangkok, Thailand
Date 12-17 June 2022
Publisher JACoW Publishing, Geneva, Switzerland
Editorial Board Frank Zimmermann (CERN, Meyrin, Switzerland); Hitoshi Tanaka (RIKEN, Hyogo, Japan); Porntip Sudmuang (SRLI, Nakhon, Thailand); Prapong Klysubun (SRLI, Nakhon, Thailand); Prapaiwan Sunwong (SRLI, Nakhon, Thailand); Thakonwat Chanwattana (SRLI, Nakhon, Thailand); Christine Petit-Jean-Genaz (CERN, Meyrin, Switzerland); Volker R.W. Schaa (GSI, Darmstadt, Germany)
Online ISBN 978-3-95450-227-1
Online ISSN 2673-5490
Received 06 June 2022
Revised 14 June 2022
Accepted 16 June 2022
Issue Date 18 June 2022
DOI doi:10.18429/JACoW-IPAC2022-TUPOST044
Pages 957-960
Creative Commons CC logoPublished by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.