Mapping neutron star data to the equation of state using the deep neural network
Mapping neutron star data to the equation of state using the deep neural network
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DOI：
10.1103/physrevd.101.054016
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发表时间：
2019-03
影响因子：
5
通讯作者：
Yuki Fujimoto;K. Fukushima;K. Murase
中科院分区：
文献类型：
--
作者：
Yuki Fujimoto;K. Fukushima;K. Murase
宇宙中最稠密的物质状态是在中子星的中心核心内独特实现的。虽然这类物质的状态方程的第一性原理评估仍然是核理论中的长期问题之一，但根据中子星现象学进行评估是可行的。在这里，我们展示了一种新的理论技术的结果，该技术利用了带监督学习的深度神经网络。我们将最新的中子星X射线辐射观测数据输入到训练好的神经网络中，并估计出压强与质量密度之间的关系。我们的结果与常规原子核模型的外推和引力波观测得到的潮汐变形性的实验界是一致的。
The densest state of matter in the Universe is uniquely realized inside the central cores of the neutron star. While first-principles evaluation of the equation of state of such matter remains as one of the long-standing problems in nuclear theory, evaluation in light of neutron star phenomenology is feasible. Here we show results from a novel theoretical technique to utilize a deep neural network with supervised learning. We input up-to-date observational data from neutron star x-ray radiations into the trained neural network and estimate a relation between the pressure and the mass density. Our results are consistent with extrapolation from the conventional nuclear models and the experimental bound on the tidal deformability inferred from gravitational wave observation.