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arXiv 1903.03400nucl-thastro-ph.HEhep-ph
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利用深度神经网络将中子星数据映射到状态方程

Mapping neutron star data to the equation of state using the deep neural network

Yuki Fujimoto, Kenji Fukushima, Koichi Murase

摘要:

宇宙中物质的致密状态是在中子星的中心核心内唯一实现的。虽然第一性原理评估的状态方程的这种物质仍然是一个长期存在的问题,在核理论,评估根据中子星现象是可行的。在这里,我们展示了一种新的理论技术的结果,利用深度神经网络进行监督学习。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,估计压力与质量密度之间的关系。我们的结果与常规核模型的外推和由引力波观测推断的潮汐形变的实验界是一致的。

英文摘要:

The densest state of matter in the universe is uniquely realized inside central cores of the neutron star. While first-principles evaluation of the equation of state of such matter remains as one of the longstanding problems in nuclear theory, evaluation in light of neutron star phenomenology is feasible. Here we show results from a novel theoretical technique to utilize 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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