Coherent synchrotron radiation (CSR) in linear accelerators (linacs) is detrimental to applications that require highly compressed beams, such as FELs and wakefield accelerators. However, traditional measurement techniques lack the precision to fully comprehend the intricate multi-dimensional aspects of CSR, particularly the varying rotation of transverse phase space slices along the longitudinal coordinate of the bunch. This study explores the effectiveness of our generative-model-based high-dimensional phase space reconstruction method in characterizing CSR effects at the Argonne Wakefield Accelerator Facility (AWA). We demonstrate that the reconstruction algorithm can successfully reconstruct beams that are affected by CSR.

Phase space reconstruction using Neural-Networks and differentiable simulation* is a robust beam diagnostic method to obtain complete 4D phase space including the coupling terms such as (x-y’) and (y-x’). In the first experimental demonstration, it was verified that the RMS beam envelope and normalized emittance from the reconstructed phase space are quantitatively similar to those from the conventional beam diagnostics such as quadrupole scan. In addition, here we show the demonstration of the phase space for the i) flat and ii) magnetized beam where the beam has i) very large ratio in between horizontal and vertical emittances (e.g., enx/eny >>1) and ii) transverse coupling induced by non-zero solenoid magnetic field at the cathode (known-as canonical momentum-dominated beam). Through the demonstrations using the experimental data achieved at the Argonne Wakefield Accelerator Facility (AWA), we successfully obtained the information such that the measured flat beam indeed has the emittance ratio larger than 70 with minimized transverse coupling. In addition, we were able to obtain the magnetization from the reconstructed phase space. Moreover, we will compare the beam parameters obtained from the phase space reconstruction and conventional diagnostics and discuss the uncertainty of the parameters.

Advances in high-performance computing have enabled detailed physics simulations, including those with nonlinear collective effects such as space charge, to be deployed online in a control room setting to aid operator intuition and be used directly in automatic tuning. Simultaneously, machine learning (ML) has enabled deployment of detailed models online with sub-second execution time, opened up new avenues for adapting simulation models to more closely match real accelerator behavior, and enabled novel ways to combine detailed physics simulations and ML-based tuning. This contribution will provide an overview of how these tools are being developed and successfully applied at SLAC, with an emphasis on experimental demonstrations. This includes improvements in adaptive calibration methods, novel approaches to simulation (e.g. differentiable physics combined with ML), and the use of system models in ML-based tuning (e.g. Bayesian optimization with system model priors, iterative simulation and ML tuning to aid LCLS-II injector commissioning). Discussion of the software infrastructure required to achieve this and deploy these solutions into regular operation will also be discussed.

Next-generation accelerator concepts, which hinge on the precise shaping of beam distributions, demand precise diagnostic methods capable of reconstructing beam distributions with 6-D phase spaces. However, the characterization of 6-D beam distributions using conventional techniques necessitates hundreds of measurements, using hours of valuable beam time. Novel diagnostic techniques are needed to reduce the number of measurements required to reconstruct detailed, high dimensional beam features for precision beam shaping applications. In this study, we present a novel approach to analyzing experimental measurements using generative machine learning models of 6-D beam distributions and differentiable beam dynamics simulations. We demonstrate in simulation that using our analysis technique, conventional beam manipulations and diagnostics can be used to reconstruct detailed 6-D phase spaces using as few as 20 beam measurements with no prior training or data collection. These developments enable detailed, high dimensional phase space information to be obtained for precision control and improved understanding of complex accelerator beam dynamics.

Coherent synchrotron radiation (CSR) in linear accelerators (linacs) is detrimental to applications that require highly compressed beams, such as FELs and wakefield accelerators. However, traditional measurement techniques lack the precision to fully comprehend the intricate multi-dimensional aspects of CSR, particularly the varying rotation of transverse phase space slices along the longitudinal coordinate of the bunch. This study explores the effectiveness of our generative-model-based high-dimensional phase space reconstruction method in characterizing CSR effects at the Argonne Wakefield Accelerator Facility (AWA). We demonstrate that the reconstruction algorithm can successfully reconstruct beams that are affected by CSR.

The recent development of advanced black box optimization algorithms has promised order of magnitude improvements in optimization speed when solving accelerator physics problems. These algorithms have been implemented in the python package Xopt, which has been used to solve online and offline accelerator optimization problems at a wide number of facilities, including at SLAC, Argonne, BNL, DESY, ESRF, and others. In this work, we describe updates to the Xopt framework that expand its capabilities and improves optimization performance in solving online optimization problems. We also discuss how Xopt has been incorporated into the Badger graphical user interface that allows easy access to these advanced control algorithms in the accelerator control room. Finally, we describe how to integrate machine learning based surrogate models provided by the LUME-model package into online optimization via Xopt.