Author: Obozinski, G.
Paper Title Page
TUPAB216 Modeling Particle Stability Plots for Accelerator Optimization Using Adaptive Sampling 1923
 
  • M. Schenk, L. Coyle, T. Pieloni
    EPFL, Lausanne, Switzerland
  • M. Giovannozzi, A. Mereghetti
    CERN, Meyrin, Switzerland
  • E. Krymova, G. Obozinski
    SDSC, Lausanne, Switzerland
 
  Funding: This work is partially funded by the Swiss Data Science Center (SDSC), project C18-07.
One key aspect of accelerator optimization is to maximize the dynamic aperture (DA) of a ring. Given the number of adjustable parameters and the compute-intensity of DA simulations, this task can benefit significantly from efficient search algorithms of the available parameter space. We propose to gradually train and improve a surrogate model of the DA from SixTrack simulations while exploring the parameter space with adaptive sampling methods. Here we report on a first model of the particle stability plots using convolutional generative adversarial networks (GAN) trained on a subset of SixTrack numerical simulations for different ring configurations of the Large Hadron Collider at CERN.
 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB216  
About • paper received ※ 19 May 2021       paper accepted ※ 17 June 2021       issue date ※ 22 August 2021  
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THPAB260 Detection and Classification of Collective Beam Behaviour in the LHC 4318
 
  • L. Coyle, F. Blanc, T. Pieloni, M. Schenk
    EPFL, Lausanne, Switzerland
  • X. Buffat, M. Solfaroli Camillocci, J. Wenninger
    CERN, Meyrin, Switzerland
  • E. Krymova, G. Obozinski
    SDSC, Lausanne, Switzerland
 
  Collective instabilities can lead to a severe deterioration of beam quality, in terms of reduced beam intensity and increased beam emittance, and consequently a reduction of the collider’s luminosity. It is therefore crucial for the operation of the CERN’s Large Hadron Collider to understand the conditions in which they appear in order to find appropriate mitigation measures. Using bunch-by-bunch and turn-by-turn beam amplitude data, courtesy of the transverse damper’s observation box (ObsBox), a novel machine learning based approach is developed to both detect and classify these instabilities. By training an autoencoder neural network on the ObsBox amplitude data and using the model’s reconstruction error, instabilities and other phenomena are separated from nominal beam behaviour. Additionally, the latent space encoding of this autoencoder offers a unique image like representation of the beam amplitude signal. Leveraging this latent space representation allows us to cluster the various types of anomalous signals.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB260  
About • paper received ※ 19 May 2021       paper accepted ※ 19 July 2021       issue date ※ 27 August 2021  
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