Author: Winklehner, D.
Paper Title Page
High-Fidelity Simulations and Machine Learning for Accelerator Design and Optimization  
  • D. Winklehner
    MIT, Cambridge, Massachusetts, USA
  • A. Adelmann
    PSI, Villigen PSI, Switzerland
  Funding: NSF #1505858, NSF #162606, NSF #1626069
Computation has become a critically important tool for particle accelerator design and optimization. Thanks to massively parallel codes running on high-performance clusters, we can now accurately predict emergent properties of particle ensembles and non-linear collective effects, and use machine learning (ML) for analysis and to create "virtual twins" of accelerator systems. Here, we will present the IsoDAR experiment in neutrino physics as an example. For it, we have developed a compact and cost-effective cyclotron-based driver to produce very high-intensity beams. The system will be able to deliver cw proton currents of 10 mA on target in the energy regime around 60 MeV. 10 mA is a factor of 10 higher than commercially available machines. This increase in current is possible due to longitudinal-radial coupling through space charge, an effect dubbed "vortex motion". We will discuss the high-fidelity OPAL simulations performed to simulate this effect in the IsoDAR cyclotron and predict beam losses due to halo formation. We will present uncertainty quantification for this design and we will show our study to optimize the IsoDAR injector RFQ using ML.
slides icon Slides TUYE6 [2.414 MB]  
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