Speaker
Christian Grech
(MXL (XFEL))
Description
Virtual diagnostics involves using fast computational tools that can predict the output of a diagnostic when it is unavailable. One work in this direction proposes the use of ML learning methods at the European XFEL's SASE1 beamline to predict X-ray properties such as beam pointing using undulator electron properties. Such an approach is promising for providing accurate knowledge on X-ray pulses of high-repetition rate XFELs. Another application for hard X-ray self-seeding operation uses a machine learning classifier to identify crystal reflections and determine the seeding energy.
Primary authors
Christian Grech
(MXL (XFEL))
Farzad Jafarinia
(MXL (XFEL))
Gianluca Aldo Geloni
(Eur.XFEL (European XFEL))
Marc Guetg
(MXL (XFEL))
Trey Guest
(Eur.GPEX)
