A major obstacle to collaboration on accelerator projects has been the sharing of lattice description files among modeling codes. To address this problem, a standardized lattice description called the Particle Accelerator Lattice Standard (PALS) is being developed. PALS development is a community-wide international effort involving accelerator physicists from multiple institutions. Along with the standard, interface packages written in commonly used languages will be developed. The importance for developing PALS is due to the increase in scale and complexity of new machines bringing an ever greater need for global collaboration, as well as interfacing with the data-driven activities using artificial intelligence and machine learning. The proposed Particle Accelerator Lattice Standard aims to promote: (i) portability between applications, (ii) a unified open-access description for scientific data (publishing and archiving), (iii) a unified description for post-processing, visualization and analysis. We will present an introduction to the effort, an overview of the standard, examples of applications, and discuss plans and future involvements from the community.

Differentiable simulations are in demand in accelerator physics, demonstrating order-of-magnitude improvements for complex tasks such as many-parameter optimization for accelerator working points and reconstruction of hard-to-measure quantities. At its core, a differentiable simulation does not only solve a forward problem, but additionally provides gradients of output parameters (e.g. beam parameters) with respect to input parameters (e.g. beamline or source parameters). How to effectively program large dynamic simulations differentiably is still an open question, but there is general consensus that a “single-source” approach aided by automatic differentiation (AD) is desirable. Addressing this, there are a) emerging domain-specific languages in machine learning that are intrinsically differentiable, and b) highly-performing & scalable, general-purpose languages like ISO C++ of existing codes. The challenge of approach a) is syntax specialization, which can limit ease of implementation & performance for physics algorithms, while b) requires additional work for AD. Performance is important for modeling high-order beam dynamics and collective effects in accelerators. We compare the fast, modern codes ImpactX (C++/Python) and Cheetah (PyTorch) using traditional, gradient-free modeling. We then show progress in introducing single-source differentiability in ImpactX using modern compiler techniques, producing performant executables for gradient-based and gradient-free modeling.