Taming complexity of nuclear profile with neural-nets
It's investigated that the density distribution of finite nuclei. Approximately 3400 nuclei have been calculated by different Skyrme-Hartree-Fock(SHF) models with different interactions. A well-designed deep neural network with a normalized condition for the certain mass based on back propagation was used to learn the data from these calculations. It is found that only about 10\% of the nuclei(300 - 400) proton(neutron) density profile are needed to describe the nuclear profile all over the nuclear chart in the case of the error within 2\% relative to saturation density. When there are about 200 proton(neutron) density profiles for training, the relative error comes to 5\%. In the process of machine learning, there is a crossover-point(we call it W-point) between Fermi-like distribution and SHF density distribution, many interesting properties of which are revealed. For different interactions, we get almost the same results. Therefore, It can be assumed that the ability to learn data is weakly dependent on the physical model.