Abstract
We discuss the implementation of a suite of virtual diagnostics at the FACET-II facility currently under commissioning at SLAC National Accelerator Laboratory. The diagnostics will be used for the prediction of the longitudinal phase space along the linac, spectral reconstruction of the bunch profile, and non-destructive inference of transverse beam quality (emittance) while using edge radiation at the injector dogleg and bunch compressor locations. These measurements will be folded into adaptive feedbacks and Machine Learning (ML)-based reinforcement learning controls to improve the stability and optimize the performance of the machine for different experimental configurations. In this paper we describe each of these diagnostics with expected measurement results that are based on simulation data and discuss progress towards implementation in regular operations.
Highlights
We present three examples of the simulated Longitudinal Phase Space (LPS) profiles at the FACET-II experimental area, as measured by the Transverse Deflecting Cavity (TCAV) shown in Figure 2 with corresponding current profiles and prediction from the Machine Learning (ML)-based virtual diagnostic
The network was trained while using the open source ML library Tensorflow, and two separate models with the same architecture were trained for the 2D LPS prediction and 1d current profile prediction
We have described a suite of ML-based and adaptive virtual diagnostics, as well as adaptive and ML based controls to be used in regular operations at the FACET-II
Summary
Adaptive model-independent feedback is a general approach that we aim to utilize together with diagnostics in order to perform active control of the FACET-II beam
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