IPAC'26 - the 17th International Particle Accelerator Conference

SciBmad: A differentiable and high performance software library for particle accelerator design, nonlinear analysis, simulation, and machine learning

THP5325

21 May 2026, 16:00

2h

C.I.D

Poster Presentation MC5.D02: Nonlinear Single Particle Dynamics Resonances, Tracking, Higher Order, Dynamic Aperture, Code Developments Poster session

Speaker

Matthew Signorelli (Cornell University (CLASSE))

Description

We present SciBmad, a new and comprehensive accelerator physics software library developed to meet the needs of all accelerator design, analysis, and virtualization. SciBmad, which consists of a set of modular packages, features fully differentiable and CPU/GPU-parallelized symplectic integrators, spin tracking and radiation, advanced (and differentiable) lattice definitions, nonlinear normal form analysis tools with Lie algebraic methods, and much more. It is fully usable in Julia and/or Python, making it easy to integrate it with external optimizers and machine learning frameworks. All packages in SciBmad are backed by rigorous, automated test suites with maximal code coverage, as well as correctness tests. With a growing list of features and contributors, SciBmad aims to be a powerful tool for any accelerator physics application.

Funding Agency

Work supported in part by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy.