论文标题
在神经网络方法中处理丢失的数据，以识别多层检测器中带电的粒子
Handling missing data in a neural network approach for the identification of charged particles in a multilayer detector
论文作者
论文摘要
通过使用神经网络，可以通过能量损失技术在多层检测器中识别带电的颗粒。当缺少大量信息时，例如由于检测器效率低下，网络的性能变得更糟。在过去的几年中，已经开发了一种算法丢失信息的算法。在各种方法中，我们专注于正常混合模型，与标准的平均插补和多种插补方法相比。此外，为了说明能量损失数据的固有不对称性，我们考虑了偏斜正常的混合模型，并在预期最大化（EM）算法框架中提供了封闭形式的实现，以处理缺失的模式。该方法已应用于六层硅检测器中乳头，kaons和质子的能量损失被认为是神经网络的输入神经元。结果是根据不同动量箱中各种物种的重建效率和纯度给出的。
Identification of charged particles in a multilayer detector by the energy loss technique may also be achieved by the use of a neural network. The performance of the network becomes worse when a large fraction of information is missing, for instance due to detector inefficiencies. Algorithms which provide a way to impute missing information have been developed over the past years. Among the various approaches, we focused on normal mixtures models in comparison with standard mean imputation and multiple imputation methods. Further, to account for the intrinsic asymmetry of the energy loss data, we considered skew-normal mixture models and provided a closed form implementation in the Expectation-Maximization (EM) algorithm framework to handle missing patterns. The method has been applied to a test case where the energy losses of pions, kaons and protons in a six-layers Silicon detector are considered as input neurons to a neural network. Results are given in terms of reconstruction efficiency and purity of the various species in different momentum bins.
