Speaker
Raymond Fliller
(Brookhaven National Laboratory)
Description
Machine Learning has proven itself as a useful technique in a variety of applications. We have used machine learning techniques to provide an RF feedforward system to the NSLS-II linac to correct for long term drifts in the system. Prior to this, the operator needed to do the correction manually. The operators participated in every aspect of the process from generating the necessary controls, to data collection, and verification. In this paper, we discuss have machine learning was used to correct drifts in the NSLS-II linac and how the operators participated in its development.
Primary author
Raymond Fliller
(Brookhaven National Laboratory)
Co-authors
Robert Rainer
(Brookhaven National Laboratory)
Mr
Charles Gardner
(Brookhaven National Laboratory)
Mr
Philip Marino
(Brookhaven National Laboratory)
Mr
Michael Santana
(Brookhaven National Laboratory)
Mr
Gary Weiner
(Brookhaven National Laboratory)
Mr
Edward Zeitler
(Brookhaven National Laboratory)
