An assisted decision-making tool for synchrotron beamline alignment based on neural networks. (1st October 2022)

• Record Type: Journal Article
• Authors: Yan, Ruyu; Yang, Yiming; Xing, Chengye; liu, Peng; Chang, Guangcai
• Is Part Of: Journal of instrumentation. Volume 17:Number 10(2022)
• Publication Date: 2022-10-01
• Subjects: Detector alignment and calibration methods (lasers, sources, particle-beams) -- Hardware and accelerator control systems -- Simulation methods and programs; Scientific apparatus and instruments -- Periodicals; 502.84
• DOI: 10.1088/1748-0221/17/10/P10033
• Languages: English
• ISSNs: 1748-0221

Abstract

Abstract: To achieve an excellent focus quality, the parameters of optical elements ( OEs ) are, in most of the synchrotron beamlines, manually adjusted. This procedure is not only time-consuming and experience-dependent but also extremely complex when various experimental requirements are involved. Responding to this challenge, we propose a new beamline alignment tool based on neural network-assisted design. This method can predict the parameters of OEs, according to experimental requirements. Specifically, the artificial neural network (ANN) training set is generated, based on SHADOW 3 and Synchrotron Radiation Workshop ( SRW ) in OASYS . Then, the magnification factor ( M ) of the focusing lens and the position ( P ) of the secondary source is predicted, using the aforesaid tool. Finally, the parameters are verified by substituting back to the OASYS . The results show that learned NNs can predict the main parameters of the OEs with high accuracy (above 97%). Then bring the parameters above back to the OASYS software to obtain the re-tracing results. Furthermore, the final focused quality at the sample point satisfies the experimental design indicators. Experimental design indicators are flux, full width at half-maximum ( FWHM ) at the sampling point and transmission efficiency. Compared to other methods, this is a successful exploration of the ANN in the field of synchrotron beamline alignment, and it is an important guide for the design of beamlines alignment.