论文标题
使用深神经网络从中子恒星物质方程式提取核物质特性
Extracting nuclear matter properties from the neutron star matter equation of state using deep neural networks
论文作者
论文摘要
如今，从中子星（NS）观测中提取核物质性能是一个重要的问题，尤其是表征对称能量的特性，这对于正确描述正确不对称的核物质至关重要。我们使用深层神经网络（DNN）来绘制冷$β$平衡ns物质与核物质特性之间的关系。假设二次依赖对同质核物质的能量的二次不对称性，并使用在ISO-Scalar和Iso-vector贡献中使用泰勒扩展最高第四阶，我们会产生一个不同实现的数据集，这些数据集的$β$ equilibrium ns ns NS Matter和相应的核物质具有。 DNN模型经过成功训练，在测试集中获得了极高的准确性。最后，使用了一种实际情况来测试DNN模型，其中在相对论平均野外方法或Skyrme力量描述中获得的一组33个核模型被送入DNN模型，并以相当准确的准确性恢复的相应核物质参数，尤其是标准偏差$σ（L _ _ {\ syms sym syms {syms {syms}}。 $σ（k _ {\ text {sat}}）= 41.02 $ meV，分别获得了对称能量的斜率和饱和度时的核问题不可压缩性。
The extraction of the nuclear matter properties from neutron star (NS) observations is nowadays an important issue, in particular, the properties that characterize the symmetry energy which are essential to describe correctly asymmetric nuclear matter. We use deep neural networks (DNNs) to map the relation between cold $β$-equilibrium NS matter and the nuclear matter properties. Assuming a quadratic dependence on the isospin asymmetry for the energy per particle of homogeneous nuclear matter and using a Taylor expansion up to fourth order in the iso-scalar and iso-vector contributions, we generate a dataset of different realizations of $β$-equilibrium NS matter and the corresponding nuclear matter properties. The DNN model was successfully trained, attaining great accuracy in the test set. Finally, a real case scenario was used to test the DNN model, where a set of 33 nuclear models, obtained within a relativistic mean field approach or a Skyrme force description, were fed into the DNN model and the corresponding nuclear matter parameters recovered with considerable accuracy, in particular, the standard deviations $σ(L_{\text{sym}})= 12.85$ MeV and $σ(K_{\text{sat}})= 41.02$ MeV were obtained, respectively, for the slope of the symmetry energy and the nuclear matter incompressibility at saturation.
