A new approach for Canadian Light Source future orbit correction system driven by neural network

Abstract

The orbit correction system (OCS) of the Canadian Light Source (CLS) comprises of 48 sets of BERGOZ beam position monitors (BPM). Each BPM has the ability to measure the position of the beam in both the X and Y directions and can record data at a rate of 1000 times per second. Inverse Response Matrix (IRM) is utilized to determine the optimal strength of the 48 sets of orbit correctors in both the X and Y directions, in order to ensure that the beam follows its desired path. The suggestion in this study is to replace the singular value decomposition (SVD) function with a neural network algorithm, which will act as the central processing unit of the orbit correction system. The training model’s design includes three hidden layers, and within each layer, there are 96 nodes. The neural network’s outputs for regular operations in CLS exhibit a mean square error (MSE) of 10 −7 . Various difficult scenarios were created to test the OCS at 8.0 mA, using offsets in different sections of the storage ring. However, the new model was able to produce the necessary orbit correctors (OC) signals without any trouble.