JLab, Newport News, Virginia, USA
Since the early 1990s, the injector of the CEBAF accelerator at Jefferson Lab has relied on a normal-conducting RF graded-beta capture section to boost the kinetic energy of the electron beam from 100 / 130 keV to 600 keV for subsequent acceleration using a cryomodule housing two superconducting 5-cell cavities similar to those used throughout the accelerator. To simplify the injector design and improve the beam quality, the normal-conducting RF capture section and the cryomodule will be replaced with a new single booster cryomodule employing a superconducting, β = 0.6, 2-cell-cavity capture section and a single, β = 0.97, 7-cell cavity. The Upgraded Injector Test Facility at Jefferson Lab is currently hosting the new cryomodule to evaluate its performance with beam before installation at CEBAF. While demonstrating satisfactory performance of the booster and good agreement with simulations, our beam test results also speak to limitations of accelerator operations in a noisy, thermally unregulated environment.
JLab, Newport News, Virginia, USA
During the operation of CEBAF, one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. Identifying an unstable cavity out of the hundreds of cavities installed at CEBAF is difficult and time-consuming. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities. A fast data acquisition system for the legacy SRF cavities is being developed which samples and reports at 5 kHz to allow for detection of transients. A prototype chassis has been installed and tested in CEBAF. An autoencoder based machine learning model is being developed to identify anomalous SRF cavity behavior. The model is presently being trained on the slow (1 Hz) data that is currently available, and a separate model will be developed and trained using the fast (5 kHz) DAQ data once it becomes available. This paper will discuss the present status of the new fast data acquisition system and results of testing the prototype chassis. This paper will also detail the initial performance metrics of the autoencoder model.