Speaker
Description
A thorough understanding of the Longitudinal Phase Space (LPS) of the
electron beam is of great advantage to any modern linear accelerator, and of
critical importance for operating a Free Electron Laser (FEL). A diagnostic system
equipped with a Transverse Deflecting Structure (TDS) allows full imaging
of a beam’s LPS. However, measurements with a TDS are not always easily
accessible and are often destructive. In this talk, we present an application of
machine learning in the form of a virtual diagnostic which allows for online extraction
of the beam’s LPS based on non-destructive measurements. We present
how such virtual diagnostics have been developed and tested for three different
accelerators: the MAX IV linac and the FELs at FERMI and SwissFEL. We
show how a single, general network architecture and training set up can be used
to reach reliable predictions of the LPS for all three facilities. For future work,
we show how virtual diagnostics could be further developed to suit the specific
needs of operations at each facility.
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