Linear accelerators with dispersive elements experience projected emittance growth due to coherent synchrotron radiation (CSR) effects which become relevant for highly compressed beams. Even though this is a widely known effect, conventional measurement techniques are not precise enough to resolve the multi-dimensional effects in detail, namely the different rotations of transverse phase space slices throughout the longitudinal coordinate of the bunch. In this work, we apply our generative-model-based six-dimensional phase space reconstruction method in the detailed measurement of CSR effects at the Argonne Wakefield Accelerator Facility in simulations. Additionally, we study the current resolution limitations of the phase space reconstruction method and perform an analysis of its accuracy and precision in simulated cases.

RadiaSoft is developing machine learning methods to improve the operation and control of industrial accelerators. Because industrial systems typically suffer from a lack of instrumentation and a noisier environment, advancements in control methods are critical for optimizing their performance. In particular, our recent work has focused on the development of pulse-to-pulse feedback algorithms for use in dose optimization for FLASH radiotherapy. The PHASER (pluridirectional high-energy agile scanning electronic radiotherapy) system is of particular interest due to the need to synchronize 16 different accelerators all with their own noise characteristics. This presentation will provide an overview of the challenges associated with RF tuning for a PHASER-like system, a description of the model used to evaluate different control schema, and our initial results using conventional methods and machine learning methods.

RadiaSoft is developing machine learning methods to improve the operation and control of industrial accelerators. Because industrial systems typically suffer from a lack of instrumentation and a noisier environment, advancements in control methods are critical for optimizing their performance. In particular, our recent work has focused on the development of pulse-to-pulse feedback algorithms for use in dose optimization for FLASH radiotherapy. The PHASER (pluridirectional high-energy agile scanning electronic radiotherapy) system is of particular interest due to the need to synchronize 16 different accelerators all with their own noise characteristics. This presentation will provide an overview of the challenges associated with RF tuning for a PHASER-like system, a description of the model used to evaluate different control schema, and our initial results using conventional methods and machine learning methods.

Next-generation accelerator concepts, which hinge on the precise shaping of beam distributions, demand equally precise diagnostic methods capable of reconstructing beam distributions within 6-dimensional position-momentum spaces. However, the characterization of intricate features within 6-dimensional beam distributions using current diagnostic techniques necessitates a substantial number of measurements, using many hours of valuable beam time. Novel phase space reconstruction techniques are needed to reduce the number of measurements required to reconstruct detailed, high-dimensional beam features in order to resolve complex beam phenomena, and as feedback in precision beam shaping applications. In this study, we present a novel approach to reconstructing detailed 6-dimensional phase space distributions from experimental measurements using generative machine learning and differentiable beam dynamics simulations. We demonstrate that this approach can be used to resolve 6-dimensional phase space distributions from scratch, using basic beam manipulations and as few as 20 2-dimensional measurements of the beam profile. We also demonstrate an application of the reconstruction method in an experimental setting at the Argonne Wakefield Accelerator, where it is able to reconstruct the beam distribution and accurately predict previously unseen measurements 75x faster than previous methods.

A common challenge in online accelerator operations is aligning beams through a series of quadrupole magnets, especially when in situ beam position monitors are not present. Accelerator operators generally use a trial-and-error approach to solve this problem by sequentially measuring the centroid deflection of the beam as a function of quadrupole strengths. This is a challenging process that necessitates dedicated effort by operational experts, requiring significant beam time and personnel resources to configure basic accelerator operations. In this work, we use Bayesian Algorithm Execution (BAX) with virtual objectives to autonomously control steering magnets at the Argonne Wakefield Accelerator to center the beam through a quadrupole triplet. This technique uses virtual objectives to reduce the number of measurements needed to converge to an optimal solution, resulting in a turn-key algorithm for finding the optimal steering configuration for a set of accelerator magnets from scratch.

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.

Many accelerator physics problems such as beamline design, beam dynamics model calibration or interpreting experimental measurements rely on solving an optimization problem that use a simulation of beam dynamics. However, it is difficult to solve high dimensional optimization problems using current beam dynamics simulations because calculating gradients of simulated objectives with respect to input parameters is computationally expensive in high dimensions. To address this problem, backwards differentiable beam dynamics simulations have been developed that enable computationally inexpensive calculations of objective gradients that are independent of the number of input parameters. In this work, we highlight current and future applications of differentiable beam dynamics simulations in accelerator physics, such as improving accelerator design, model calibration, and machine learning. We also describe current collaborative efforts between SLAC, DESY, KIT, and LBNL to implement fast, backwards differentiable beam dynamics simulations in Python. These tools will enable unprecedented improvements in optimization efficiency and speed when using beam dynamics simulations, leading to enhanced control and detailed understanding of physical accelerator systems.

Linear accelerators with dispersive elements experience projected emittance growth due to coherent synchrotron radiation (CSR) effects which become relevant for highly compressed beams. Even though this is a widely known effect, conventional measurement techniques are not precise enough to resolve the multi-dimensional effects in detail, namely the different rotations of transverse phase space slices throughout the longitudinal coordinate of the bunch. In this work, we apply our generative-model-based six-dimensional phase space reconstruction method in the detailed measurement of CSR effects at the Argonne Wakefield Accelerator Facility in simulations. Additionally, we study the current resolution limitations of the phase space reconstruction method and perform an analysis of its accuracy and precision in simulated cases.