Applying fully convolutional networks for beam profile and emittance measurements

Abstract

Abstract The transverse cross-sectional size and emittance are critical beam parameters that characterize the performance of the accelerator and assess the state of the beam. Inspired by the success of machine learning in image processing tasks, we have crafted a bespoke measurement system with a primary focus on accurately determine the transverse cross-sectional size and emittance of the beam. The system utilizes a beam spot detector to convert the beam spot to a light spot image, which is then projected onto the CCD camera through the telecentric lens for the acquisition. The image data collected by the camera is subsequently imported into the EPICS database developed based on ADAravis software. We employ the Gaussian fitting technique on the collected images to accurately calculate the cross-sectional size of the beam. Furthermore, by incorporating the four-level iron scanning method, the lateral emittance of the beam is calculated in a comprehensive manner. To suppress the salt and pepper noise introduced due to the presence of dark current and beam shooting phenomena on the transmission line, we propose a novel fully convolutional neural network (FCN) design with preactivated residual units. The test conducted at HLS-II confirms that the measurement uncertainty of this system is superior to 27.5 μm. Moreover, when operating at an electron beam energy of 800 MeV, the measured emittance of the accelerator is found to be 38.515 nm·rad, a value closely aligning with the theoretical value of 36.2 nm·rad. These compelling results provide strong evidence supporting the reliability of the emittance measurement algorithm, making it suitable for deployment in the forthcoming terahertz accelerator.