Beam Detection Based on Machine Learning Algorithms

The free electron laser(FEL) at Stanford Linear Accelerator Center(SLAC) is an ultra-fast X-ray laser. As one of the most advanced X-ray light source [5] [6], it is famous for its high brightness and short pulse duration: it is 10 billion times brighter than the world's second brightest light source; the pulse duration is several tens femtoseconds.It plays a pivotal role in both fundamental science research and applied research [6]. The mechanism behind this laser is very delicate [5]. Thus to keep the laser in optimal working condition is challenging.The positions of the electron beams and the laser beams are of fundamental importance in the control and maintenance of this FEL. Currently, the task of locating beam spots heavily depends on human labor. This is mainly attributed to the wide varieties of beam spots and the presentation of strong noises as demonstrated in Figure 1, where the white square marks the boundary of the beam spot. Each picture requires a long sequence of signal processing methods to mark the beam position.