Conveners
Methods: Methods 1
- Annika Eichler (Deutschles Elektronen Synchrotron DESY)
- Kishansingh Rajput (Jefferson Lab)
- Jason St. John (Fermilab)
- Annika Eichler (DESY)
Methods: Methods
- Annika Eichler (DESY)
- Jason St. John (Fermilab)
- Kishansingh Rajput (Jefferson Lab)
- Annika Eichler (Deutschles Elektronen Synchrotron DESY)
Detailed modeling of particle accelerators can benefit from parallelization on modern compute hardware such as GPUs and can often be distributed to large supercomputers. Providing production-quality implementations, the Beam, Plasma & Accelerator Simulation Toolkit (BLAST) provides multiple modern codes to cover the widely different time and length scales between conventional accelerator...
Physics simulations of particle accelerators enable predictions of beam dynamics in a high degree of detail.This can include the evolution of the full 6D phase space distribution of the beam under the impact of nonlinear collective effects, such as space charge and coherent synchrotron radiation. However, despite the high fidelity with respect to the expected beam physics, it is challenging to...
Typical operational environments for industrial particle accelerators are less controlled than those of research accelerators. This leads to increased levels of noise in electronic systems, including radio frequency (RF) systems, which make control and optimization more difficult. This is compounded by the fact that industrial accelerators are mass-produced with less attention paid to...
This study introduces an innovative approach that harnesses machine learning in conjunction with Differential Algebraic (DA) techniques to simulate beam dynamics in particle accelerators. Beam dynamics simulations are complex, involving high-dimensional phase spaces and intricate equations of motion. By integrating the DA method, which deals with function derivatives, with machine learning, we...
Machine learning has emerged as a powerful solution to the modern challenges in accelerator physics. However, the limited availability of beam time and the high computational cost of simulation codes pose significant hurdles in generating the necessary data for training state-of-the-art machine learning models. Furthermore, optimisation methods can be used to tune accelerators and perform...
The design of novel accelerator components such as ionisation cooling channel for a muon collider necessitates extensive simulation and optimization studies. We present the application of Bayesian Optimization and surrogate model - based evaluation of lattice parameters, which allowed to surpass the baseline cooling performance. Robust emittance estimation throughout the cooling channel is...
