PACMAN HIPA

Particle Accelerators and Machine Learning

Started
January 2, 2019
Status
In Progress
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Abstract

The High Intensity Proton Accelerator (HIPA) at Paul Scherrer Institute (PSI) provides the primary beams to PSI’s versatile experimental facilities which in turn provide high intensity beams for research. In an accelerator control room several hundred of continuous sensor data are displayed in order to aid the operators in running the accelerator with maximal performance. We propose to bring Machine Learning (ML) to particle accelerator operation, in order to increase the performance. A more accurate parameter control based on the surrogate modelling will contribute to reliable and safe operation, and increase the accelerator efficiency. The immediate benefits will be: reducing the risks related to the high beam power by reducing the activation and beam losses, an action that will in turn, lead to fewer machine interruptions and possibly higher beam intensities. The project is likely to have a game-changing impact in howwe model and operate charged particle accelerators in the near future.

People

Collaborators

SDSC Team:
Fernando Perez-Cruz

PI | Partners:

Cyclotron Development and Beam Dynamics:

  • Dr. Jochem Snuverink
  • Dr. Nicole Hiller

More info

Laboratory for Scientific Computing and Modelling:

  • Dr. Andreas  Adelmann
  • Dr. Jaime Coello de Portugal
  • Sichen Li

More info

Learning & Adaptive Systems Group:

  • Prof. Andreas Krause
  • Johannes Kirschner
  • Mojmir Mutny

More info

description

Goals:

  1. Minimise beam losses: To be able to predict the reaction of a knob, especially those at the first sections of the accelerator, a reliable machine model needs to be available.
  2. Better control of accelerator parameters: we will establish a fast on-line enhancement of the machine protection system with beam diagnostics data. The accelerator parameters will be predicted from the beam diagnostics data. Appropriate changes to the machine / beamline settings will be proposed as operational enhancements by the surrogate model.
  3. Prevent unnecessary machine interruptions: A surrogate model that captures fast responses from the machine can be used in the forecasting of machine interruptions. Here we would expand from research goal 1 and use results from the fusion community [10] (and the references therein).
Source: PSI

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