Machine learning has emerged as a powerful solution to the modern challenges
in accelerator physics. However, the limited availability of beam time, the
computational cost of simulations, and the high-dimensionality of optimisation
problems pose significant challenges in generating the required data for
training state-of-the-art machine learning models. In this work, we introduce
Cheetah, a PyTorch-based high-speed differentiable linear-beam dynamics code.
Cheetah enables the fast collection of large data sets by reducing computation
times by multiple orders of magnitude and facilitates efficient gradient-based
optimisation for accelerator tuning and system identification. This positions
Cheetah as a user-friendly, readily extensible tool that integrates seamlessly
with widely adopted machine learning tools. We showcase the utility of Cheetah
through five examples, including reinforcement learning training,
gradient-based beamline tuning, gradient-based system identification,
physics-informed Bayesian optimisation priors, and modular neural network
surrogate modelling of space charge effects. The use of such a high-speed
differentiable simulation code will simplify the development of machine
learning-based methods for particle accelerators and fast-track their
integration into everyday operations of accelerator facilities.

Cheetah 是基于 PyTorch 的高速可微线性束线动力学代码，通过多个量级减少计算时间，为加速器调谐和系统识别提供了高效的梯度优化方式，并且可与广泛采用的机器学习工具无缝集成，简化了基于机器学习方法为粒子加速器开发并快速融入日常运营的过程。