论文标题
使用图像压缩和机器学习的Clara加速器测试设施中的横时空间断层扫描
Transverse phase space tomography in the CLARA accelerator test facility using image compression and machine learning
论文作者
论文摘要
我们根据图像压缩和机器学习来描述一种新型技术，用于在加速器光束线中以两个自由度为两种自由度的横时空间断层扫描。该技术已在Daesbury实验室的Clara Accelerator测试设施中使用：将机器学习方法的结果与传统层析成像算法（代数重建）的结果进行了比较，该算法应用于相同的数据。机器学习的使用允许重建梁的4D相空间分布比使用常规层析成像算法要快得多，还可以使使用图像压缩以显着减少分析中涉及的数据集的大小。机器学习技术的结果至少与代数重建断层扫描中梁行为的表征相同，这是响应于四极强度的变化而变化的梁大小的变化。
We describe a novel technique, based on image compression and machine learning, for transverse phase space tomography in two degrees of freedom in an accelerator beamline. The technique has been used in the CLARA accelerator test facility at Daresbury Laboratory: results from the machine learning method are compared with those from a conventional tomography algorithm (algebraic reconstruction), applied to the same data. The use of machine learning allows reconstruction of the 4D phase space distribution of the beam to be carried out much more rapidly than using conventional tomography algorithms, and also enables the use of image compression to reduce significantly the size of the data sets involved in the analysis. Results from the machine learning technique are at least as good as those from the algebraic reconstruction tomography in characterising the beam behaviour, in terms of the variation of the beam size in response to variation of the quadrupole strengths.
