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The application of encoder–decoder neural networks in high accuracy and efficiency slit-scan emittance measurements

Ma, S.; Arnold, A.; Michel, P.; Murcek, P.; Ryzhov, A.; Schaber, J.; Steinbrück, R.; Evtushenko, P.; Teichert, J.; Hillert, W.; Xiang, R.; Zhu, J.

Abstract

A superconducting radio-frequency (SRF) photo injector is in operation at the electron linac for beams with high brilliance
and low emittance (ELBE) radiation center and generates continuous wave (CW) electron beams with high average current
and high brightness for user operation since 2018. The speed of emittance measurement at the SRF gun beamline can be
increased by improving the slit-scan system, thus the measurement time for one phase space mapping can be shortened
from about 15 min to 90 s. The convolution neural networks are applied to improve the efficiency and accuracy of beamlet
images processing. In order to estimate the uncertainty in the calculation of normalized emittance, we analyze the main error
contributions.

Keywords: Beam emittance; Machine learning; Slit-scan; SRF photo injectors

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Permalink: https://www.hzdr.de/publications/Publ-36917

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