The LCLS-II Gun & Buncher LLRF Controller Upgrade

LCLS-II is currently in its commissioning phase at SLAC. It is an X-ray FEL driven by a CW superconducting LINAC. The beam injector plays a crucial role in the overall performance of the accelerator, and is critical to the final electron beam performance parameters. The LCLS-II injector comprises of a 185.7 MHz VHF copper gun cavity, and a 1.3 GHz two-cell L-band copper buncher cavity. The FPGA-based controller employs feedback and Self-Excited Loop logic in order to regulate the cavity fields. It also features several other functionalities, such as live detune computation, active frequency tracking, and waveform recording. The LLRF system drives the cavities via two 60 kW SSAs through two power couplers, and thus stabilizes the fields inside the plant. This paper provides an outline of the general functionalities of the system, alongside a description of its hardware, firmware and software architecture, before finalizing with the current status of the project and its future goals.Comment: Poster presented at LLRF Workshop 2022 (LLRF2022, arXiv:2208.13680

An adaptive approach to machine learning for compact particle accelerators.

Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics

Digital Low-Level RF control system for Accumulator Ring at Advanced Light Source Upgrade Project

Currently ALS is undergoing an upgrade to ALSU to produce 100 times brighter soft X-ray light. The LLRF system for Accumulator Ring (AR) is composed of two identical LLRF stations, for driving RF amplifiers. The closed loop RF amplitude and phase stability is measured as < 0.1\% and < 0.1^\circ respectively, using the non-IQ digital down conversion together with analog up/down conversion, under a system-on-chip architecture. Realtime interlock system is implemented with < 2 \mus latency, for machine protection against arc flash and unexpected RF power. Control interfaces are developed to enable PLC-FPGA-EPICS communication to support operation, timing, cavity tuning, and interlock systems. The LLRF system handles alignment of buckets to swap beams between AR and Storage Ring by synchronous phase loop ramping between the two cavities. The system also includes an optimization routine to characterize the loop dynamics and determine optimal operating point using a built-in network analyzer feature. A cavity emulator of 31 kHz bandwidth is integrated with the LLRF system to validate the performance of the overall system being developed