Beam distributions beyond RMS
Beam distributions beyond RMS
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光束分布超出 RMS
DOI：
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发表时间：
1994
期刊：
影响因子：
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通讯作者：
F. Decker
中科院分区：
文献类型：
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作者：
F. Decker
波束通常仅由其分布的位置（平均值）和宽度（rms=均方根）表示。为了在具有高背景的噪声条件下实现这些光束参数，将具有偏移的高斯分布（4个参数）拟合到测量的光束分布。这给出了非常稳健的答案，并且对背景减除技术不太敏感。为了获得分布的较高矩，如偏斜或峰度，需要具有一个或两个以上参数的拟合函数，其将对较高矩进行建模。在本文中，我们将集中讨论非对称高斯函数和超高斯函数，它们将给出分布的偏斜和峰度。该信息用于量化特殊光束分布。有些是不需要的，如来自横向尾流场的束尾（偏斜）、高阶色散像差或阻尼环中的势阱畸变。负峰度的光束分布描述了一个更矩形，紧凑的形状。
The beam is often represented only by its position (mean) and the width (rms=root mean squared) of its distribution. To achieve these beam parameters in a noisy condition with high backgrounds, a Gaussian distribution with offset (4 parameters) is fitted to the measured beam distribution. This gives a very robust answer and is not very sensitive to background subtraction techniques. To get higher moments of the distribution, like skew or kurtosis, a fitting function with one or two more parameters is desired which would model the higher moments. In this paper we will concentrate on an Asymmetric Gaussian and a Super Gaussian function that will give something like the skew and the kurtosis of the distribution. This information is used to quantify special beam distribution. Some are unwanted like beam tails (skew) from transverse wakefields, higher order dispersive aberrations or potential well distortion in a damping ring. A negative kurtosis of a beam distribution describes a more rectangular, compact sha...