Coherent synchrotron radiation has a significant impact on electron storage rings and bunch compressors, inducing energy spread and emittance growth in a bunch. While the physics of the phenomenon is well-understood, numerical calculations are computationally expensive, severally limiting their usage. Here, we explore utilizing neural networks (NNs) to model the 3D wakefields of electrons in circular orbit in the steady state condition. We demonstrate that NNs can achieve a significant speed-up, while also accurately reproducing the 3D wakefields. NN models were developed for both Gaussian and general bunch distributions. These models can potentially aid in the design and optimization of accelerator apparatuses by enabling rapid searches through parameter space.

A very high electron peak current is needed in many applications of modern electron accelerators. To achieve this high current, a large energy chirp must be imposed on the bunch so that the electrons will compress when they pass through a chicane. In existing linear accelerators (LINACs), this energy chirp is imposed by accelerating the beam off-crest from the peak fields of the RF cavities, which increases the total length and power requirements of the LINAC. A novel concept known as the Transverse Deflecting Cavity Based Chirper (TCBC) [1] can be used to actively impose a large energy chirp onto an electron beam in an accelerator, without the need for off-crest acceleration. The TCBC consists of 3 transverse deflecting cavities, which together impose an energy chirp while cancelling out the transverse deflection. An experiment is being developed to demonstrate this concept at the Argonne Wakefield Accelerator (AWA) facility. Here we explain the concept, show preliminary simulations of the experiment, and report on progress related to implementation of the experiment at AWA.

Single crystal photocathode films promise to lower the mean transverse energy (MTE) of emitted electrons by tens of millielectronvolts, offering significant benefits for advanced light sources and electron microscopes. Traditional methods for assessing the surface quality involve accelerating electrons in a DC gap and inferring their angular distribution from the beam spot size. However, the accuracy of this approach is limited by the finite spot size of the photon beam at the cathode. To overcome this limitation, we capture a series of electron distributions on a screen at varying accelerating voltages. Each distribution corresponds to a convolution of the electron momentum distribution and the intensity distribution of light at the cathode. The relative contributions of these factors depend on the applied voltage, enabling us to reconstruct both the momentum distribution of electrons and the intensity of light to best match the observations. We conclude that it enables the measurement of the momentum distribution of photoemitted electrons with a resolution of about 5 meV in a reasonable momentatron geometry.

Particle accelerators are among the largest and most expensive scientific facilities. Constant monitoring of data from a diverse array of diagnostics is imperative to ensure proper operational parameters—such as beam parameters, power sources, cooling systems, etc. Detecting equipment failure within this data stream is challenging due to the accelerator parameters gradually shifting over time due to diverse user demands, environmental factors, and the feedback control system's operation. At LANSCE, identifying anomalies stemming from deteriorating equipment is a significant issue. To address this, we propose implementing an anomaly detection system based on existing machine learning algorithms. This system will monitor all available data for each accelerator subsystem, establish typical parameter ranges, and determine whether the measured parameters fall beyond those thresholds. This anomaly detection system aims to factor in intrinsic internal correlations among various parameters, which the current Data Watcher warning system fails to consider. We anticipate that this developed warning system will effectively identify ongoing equipment degradation and predict upcoming failures.