部分子分布函数的新途径：自组织映射
New avenue to the Parton Distribution Functions: Self-Organizing Maps
摘要：
神经网络算法最近已被应用于构造部分子分布函数（PDF）的参数化，提供了一种替代标准的全球拟合程序。我们提出了一种基于自组织映射（SOM）的交互式神经网络算法的技术。SOM是一类基于空间有序神经元竞争学习的聚类算法。我们的SOM在随机生成的PDF样本的选择上进行训练。每个优化迭代的选择标准是基于聚簇PDF的特征。我们的主要目标是提供一个拟合过程，在与标准的神经网络方法的差异，允许通过在过程的各个阶段启用用户交互来增加对系统偏差的控制。
英文摘要：
Neural network algorithms have been recently applied to construct Parton Distribution Function (PDF) parametrizations which provide an alternative to standard global fitting procedures. We propose a technique based on an interactive neural network algorithm using Self-Organizing Maps (SOMs). SOMs are a class of clustering algorithms based on competitive learning among spatially-ordered neurons. Our SOMs are trained on selections of stochastically generated PDF samples. The selection criterion for every optimization iteration is based on the features of the clustered PDFs. Our main goal is to provide a fitting procedure that, at variance with the standard neural network approaches, allows for an increased control of the systematic bias by enabling user interaction in the various stages of the process.
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