Machine Learning For Beamline Steering
Isaac Kante
Beam steering is the process involving the calibration of the angle and
position at which a particle accelerator's electron beam is incident upon the
x-ray target with respect to the rotation axis of the collimator. Beam Steering
is an essential task for light sources. In the case under study, the LINAC To
Undulator (LTU) section of the beamline is difficult to aim. Each use of the
accelerator requires re-calibration of the magnets in this section. This
involves a substantial amount of time and effort from human operators, while
reducing scientific throughput of the light source. We investigate the use of
deep neural networks to assist in this task. The deep learning models are
trained on archival data and then validated on simulation data. The performance
of the deep learning model is contrasted against that of trained human
operators.