Speakers
Description
Accurate and high resolution detection of the Longitudinal Phase Space (LPS) of the electron beam is a great advantage for operating and setting up a FEL. In the case of the soft X-ray FEL being proposed at the MAX IV synchrotron facility in Lund, this information is mainly supplied by a Transverse Deflecting Cavity (TDC) which is currently being installed and scheduled for commissioning in the autumn. Performing the LPS measurement with the future TDC is limited in two regards: it is destructive and may be low in resolution as compared to the maximum compression possible in the MAX IV linac. In this project we propose using machine learning tools to implement a virtual diagnostic to retrieve the LPS information non-destructively using fast, non-invasive measurements and critical set-points in the linac as inputs for a neural network. In this paper we summarize the current progress of this project which has thus far focused on simulation studies of the TDC and the training of a virtual diagnostic using the TDC's simulated output.
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