论文标题
人工神经网络中的核结合能
Nuclear binding energies in artificial neural networks
论文作者
论文摘要
结合能（BE）或质量是原子核最基本的特性之一。精确的结合能是许多核物理和核天体物理学研究的重要输入。但是，由于原子核的复杂性和非扰动强相互作用的复杂性，直到现在，迄今为止，没有传统的物理模型可以用低于0.1 MeV的精度来描述核结合能，这是核天体物理学所需的准确性。 In this work, artificial neural networks (ANNs), the so called ``universal approximators", are used to calculate nuclear binding energies. We show that the ANN can describe all the nuclei in AME2020 with a root-mean-square deviation (RMSD) around 0.2 MeV, which is better than the best macroscopic-microscopic models, such as FRDM and WS4. The success of the ANN is mainly due to the我们识别的适当的输入特征，其中包含最相关的物理信息，即外壳，削皮和同性恋 - 对称效应，我们表明，训练有素的ANN具有出色的外推能力，并且可以预测这些核的结合能力，以预测到目前为止的界限。
The binding energy (BE) or mass is one of the most fundamental properties of an atomic nucleus. Precise binding energies are vital inputs for many nuclear physics and nuclear astrophysics studies. However, due to the complexity of atomic nuclei and of the non-perturbative strong interaction, up to now, no conventional physical model can describe nuclear binding energies with a precision below 0.1 MeV, the accuracy needed by nuclear astrophysical studies. In this work, artificial neural networks (ANNs), the so called ``universal approximators", are used to calculate nuclear binding energies. We show that the ANN can describe all the nuclei in AME2020 with a root-mean-square deviation (RMSD) around 0.2 MeV, which is better than the best macroscopic-microscopic models, such as FRDM and WS4. The success of the ANN is mainly due to the proper and essential input features we identify, which contain the most relevant physical information, i.e., shell, paring, and isospin-asymmetry effects. We show that the well-trained ANN has excellent extrapolation ability and can predict binding energies for those nuclei so far inaccessible experimentally. In particular, we highlight the important role played by ``feature engineering'' for physical systems where data are relatively scarce, such as nuclear binding energies.
