Speaker
Description
High-fidelity Monte-Carlo simulations of beamlines are critical for precise particle transport studies, but they are often labor-intensive - especially for dynamically configurable setups such as those in secondary beamlines at CERN. Sophisticated tools for optics calculations, geometry descriptions, and particle transport simulations exist, yet an integrated framework that combines these elements to create detailed, reproducible beamline models has remained elusive. We present 'eadevices', a new Python framework developed at CERN to automate and standardise high-fidelity beamline modelling. It includes a database of detailed 3D geometries and corresponding magnetic field maps for a wide range of beamline components. By leveraging a modular sandbox architecture, eadevices enables seamless integration of these components and any accompanying field maps into complete accelerator and beamline models. By automating the workflow and bridging established accelerator and particle physics tools - such as MAD-X, Geant4, BDSIM, and pyg4ometry - the framework significantly reduces the need for manual intervention and accelerates the beamline modelling process. With eadevices, beam physicists can now generate reliable, reproducible simulation models of dynamically configurable beamlines in a fraction of the time previously required.
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