研究部分子分布函数的新方法：自组织映射
New approach to the Parton Distribution Functions: Self-Organizing Maps
摘要：
我们提出了一个部分子分布函数（PDF）的拟合技术，这是基于一个交互式神经网络算法使用自组织映射（SOM）。SOM是基于空间有序神经元之间竞争学习的可视化算法。我们的SOM是用随机生成的PDF样本训练的。在每次优化迭代中，PDF根据用户定义的特征在SOM上聚类，并选择最有希望的候选者作为后续迭代的种子。因此，我们的主要目标是提供一个拟合过程，在与全球分析和标准神经网络方法的差异，允许通过在拟合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：
We propose a Parton Distribution Function (PDF) fitting technique which is based on an interactive neural network algorithm using Self-Organizing Maps (SOMs). SOMs are visualization algorithms based on competitive learning among spatially-ordered neurons. Our SOMs are trained with stochastically generated PDF samples. On every optimization iteration the PDFs are clustered on the SOM according to a user-defined feature and the most promising candidates are selected as a seed for the subsequent iteration. Our main goal is thus to provide a fitting procedure that, at variance with the global analyses and standard neural network approaches, allows for an increased control of the systematic bias by enabling user interaction in the various stages of the fitting process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
