Automatic differentiation computes beamline optimization gradients faster than finite differences on CPU and usually on GPU.
JuTrack: a Julia package for auto-differentiable accelerator modeling and particle tracking
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abstract
Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JuTrack, a nested accelerator modeling package developed in the Julia programming language and enhanced with compiler-level automatic differentiation (AD). With the aid of AD, JuTrack enables rapid derivative calculations in accelerator modeling, facilitating sensitivity analyses and optimization tasks. We demonstrate the effectiveness of AD-derived derivatives through several practical applications, including sensitivity analysis of space-charge-induced emittance growth, nonlinear beam dynamics analysis for a synchrotron light source, and lattice parameter tuning of the future Electron-Ion Collider (EIC). Through the incorporation of automatic differentiation, this package opens up new possibilities for accelerator physicists in beam physics studies and accelerator design optimization.
fields
physics.acc-ph 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Differentiable simulations for particle tracking in accelerators: analysis, benchmarking and optimization
Automatic differentiation computes beamline optimization gradients faster than finite differences on CPU and usually on GPU.