Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations

Abstract

Characterizing the phase space distribution of particle beams in accelerators is a central part of accelerator understanding and performance optimization. However, conventional reconstruction-based techniques either use simplifying assumptions or require specialized diagnostics to infer high-dimensional ($>$ 2D) beam properties. In this Letter, we introduce a general-purpose algorithm that combines neural networks with differentiable particle tracking to efficiently reconstruct high-dimensional phase space distributions without using specialized beam diagnostics or beam manipulations. We demonstrate that our algorithm accurately reconstructs detailed 4D phase space distributions with corresponding confidence intervals in both simulation and experiment using a single focusing quadrupole and diagnostic screen. This technique allows for the measurement of multiple correlated phase spaces simultaneously, which will enable simplified 6D phase space distribution reconstructions in the future.

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• Transverse phase space tomography in the CLARA accelerator test facility using image compression and machine learning
• Four-Dimensional Emittance Measurements of Ultrafast Electron Diffraction Optics Corrected Up to Sextupole Order
• Electron beam transverse phase space tomography using nanofabricated wire scanners with submicrometer resolution

Cited by in corpus (10)

• Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations
• Four-Dimensional Phase-Space Reconstruction of Flat and Magnetized Beams Using Neural Networks and Differentiable Simulations
• Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming
• Bayesian Optimization Algorithms for Accelerator Physics
• Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal
• Experimental demonstration of a tomographic 5D phase-space reconstruction
• Experimental demonstration of cascaded round-to-flat and flat-to-round beam transformations
• Neural-network-based longitudinal electric field prediction in nonlinear plasma wakefield accelerators
• Deployment and validation of predictive 6-dimensional beam diagnostics through generative reconstruction with standard accelerator elements
• The Memory Scaling of Reverse-Mode Differentiation in Particle Accelerator Simulations with Space Charge