论文标题
具有深神经网络方法的驯服核密度分布
Taming nucleon density distributions with deep neural network method
论文作者
论文摘要
我们研究了使用精心设计的深神经网络方法的有限核的密度分布。我们使用用于训练网络的Skyrme密度函数理论计算目标核密度分布。我们发现，仅$ 10 \％$ nuclei（$ 300-400 $）的培训足以描述在2 \％相对误差范围内所有核图表的核密度分布。当大约200个质子（中子）密度分布用于训练时，相对误差为5 \％。对于不同的Skyrme密度功能理论，我们获得了非常相似的结果。因此，训练网络的能力微弱取决于理论模型。此外，在机器学习的过程中，有一个转折点，显示了从类似费米的分布到现实的天际分布的过渡，该分布提供了收敛过程的重要特性。
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.
