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

R Roussel¹, A Edelen¹, C Mayes¹

¹SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA.

Physical Review Letters

|April 21, 2023

Summary

This summary is machine-generated.

This study presents a new algorithm using neural networks and differentiable particle tracking to reconstruct high-dimensional particle beam phase space distributions. The method accurately measures 4D distributions with confidence intervals, enabling future 6D reconstructions.

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Area of Science:

Accelerator physics
Computational physics
Data analysis

Background:

Accurate characterization of particle beam phase space is crucial for accelerator performance.
Traditional methods struggle with high-dimensional distributions or require specialized equipment.
Inferring beam properties often involves simplifying assumptions or complex diagnostics.

Purpose of the Study:

To develop a general-purpose algorithm for efficient reconstruction of high-dimensional particle beam phase space distributions.
To overcome limitations of conventional methods in characterizing beam dynamics.
To enable accurate measurement of correlated phase spaces without specialized diagnostics.

Main Methods:

Integration of neural networks with differentiable particle tracking.
Development of a general-purpose algorithm for phase space reconstruction.
Utilizing limited measurements from a single focusing quadrupole and diagnostic screen.

Main Results:

Accurate reconstruction of detailed 4D phase space distributions.
Successful demonstration in both simulation and experimental data.
Provision of confidence intervals for the reconstructed distributions.
Capability to measure multiple correlated phase spaces simultaneously.

Conclusions:

The novel algorithm efficiently reconstructs high-dimensional phase space distributions.
This technique simplifies the measurement of beam properties without specialized diagnostics.
The method paves the way for future simplified 6D phase space distribution reconstructions.