论文标题
在高精度和效率缝隙扫描测量值中，将编码器描述器神经网络应用于
The application of encoder-decoder neural networks in high accuracy and efficiency slit-scan emittance measurements
论文作者
论文摘要
在电子LINAC上进行了超导射频（SRF）照片注射器，用于具有高光彩和低排放性（Elbe）辐射中心的光束，并产生连续波（CW）电子光束，具有高平均电流和高亮度以自2018年以来的速度测量时间。映射可以从大约15分钟到90秒缩短。平行算法和机器学习已被用来降低Beamlet图像噪声。为了估计归一化发射率计算的不确定性，我们分析了主要误差贡献，例如狭缝位置不确定性，图像噪声，空间电荷效应和能量测量不准确。
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 minutes to 90 seconds. A parallel algorithm and machine learning have been used to reduce the beamlet image noise. In order to estimate the uncertainty in the calculation of normalized emittance, we analyze the main error contributions such as slit position uncertainty, image noise, space charge effects and energy measurement inaccuracy.
