LHC的CMS跟踪器中用高斯和滤波器重建电子
Reconstruction of electrons with the Gaussian-sum filter in the CMS tracker at LHC
摘要：
电子在物质中传播的韧致辐射能量损失分布是高度非高斯的。由于卡尔曼滤波器仅依赖于高斯概率密度函数，因此它可能不是电子径迹的最佳重建算法。因此，高斯和滤波器（GSF）的CMS跟踪器中的电子径迹重建算法已经开发。其基本思想是通过高斯混合而不是单个高斯来模拟韧致辐射能量损失分布。结果表明，GSF是能够提高电子的动量分辨率相比，标准卡尔曼滤波器。研究了能量损失分布的各种混合模型的动量分辨率和估计误差的质量。
英文摘要：
The bremsstrahlung energy loss distribution of electrons propagating in matter is highly non Gaussian. Because the Kalman filter relies solely on Gaussian probability density functions, it might not be an optimal reconstruction algorithm for electron tracks. A Gaussian-sum filter (GSF) algorithm for electron track reconstruction in the CMS tracker has therefore been developed. The basic idea is to model the bremsstrahlung energy loss distribution by a Gaussian mixture rather than a single Gaussian. It is shown that the GSF is able to improve the momentum resolution of electrons compared to the standard Kalman filter. The momentum resolution and the quality of the estimated error are studied with various types of mixture models of the energy loss distribution.
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