论文标题
通过深度学习模型研究质子的Parton含量
Studying the parton content of the proton with deep learning models
论文作者
论文摘要
Parton分布函数（PDFS）模拟质子的Parton含量。在侧重于PDF确定的众多合作中，NNPDF率先使用神经网络来建模具有给定能量和动量的质子内部发现Partons（Quarks and Gluons）的概率。在此程序中，我们利用最先进的技术现代化NNPDF方法论并研究不同的模型和优化器，以提高PDF的质量：提高拟合的质量和效率。我们还提供了Evolutionary_keras库，这是NNPDF使用的进化算法的KERAS实现。
Parton Distribution Functions (PDFs) model the parton content of the proton. Among the many collaborations which focus on PDF determination, NNPDF pioneered the use of Neural Networks to model the probability of finding partons (quarks and gluons) inside the proton with a given energy and momentum. In this proceedings we make use of state of the art techniques to modernize the NNPDF methodology and study different models and optimizers in order to improve the quality of the PDF: improving both the quality and efficiency of the fits. We also present the evolutionary_keras library, a Keras implementation of the Evolutionary Algorithms used by NNPDF.
