arXiv 2012.073342020-12-26nucl-th

用深度神经网络方法驯服核子密度分布

Taming nucleon density distributions with deep neural network method

Zu-Xing Yang, Wei Zuo, Peng Yin, Xiao-Hua Fan

备注：: 7 pages, 6 figures
DOI：: 10.1016/j.physletb.2021.136650

摘要：

我们采用一个精心设计的深度神经网络方法研究有限核的密度分布。我们用Skyrme密度泛函理论计算了靶核子的密度分布，并将其用于网络的训练。我们发现，仅用大约10个$10\%$核（$300-400$）进行训练，就足以描述所有核图的核子密度分布，相对误差在2%相对误差以内.当使用约200个质子（中子）密度分布进行训练时，其相对误差为5%。对于不同的Skyrme密度泛函理论，我们得到了非常相似的结果。因此，训练网络的能力弱依赖于理论模型。此外，在机器学习过程中，存在一个转折点，表明从Fermi-like分布过渡到现实的Skyrme分布，这提供了收敛过程的重要性质。

英文摘要：

We investigate the density distributions of finite nuclei employing a well-designed deep neural network method. We calculate the target nucleon density distributions with Skyrme density functional theories, which are used to train the networks. We find that the training with only about $10\%$ nuclei ($300-400$) is sufficient to describe the nucleon density distributions of all the nuclear chart within 2\% relative error. The relative error comes to 5\% when about 200 proton(neutron) density distributions are used for training. We obtained very similar results for different Skyrme density functional theories. Therefore the ability to train networks is weakly dependent on the theoretical model. Moreover, in the process of machine learning, there is a turning point showing the transition from the Fermi-like distribution to the realistic Skyrme distribution, which provides significant properties of convergence 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。

摘要：

英文摘要：

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