论文标题
中子星可观察的状态密集物质方程的神经网络重建
Neural network reconstruction of the dense matter equation of state from neutron star observables
论文作者
论文摘要
在核天体物理学领域，强烈相互作用的冷和热QCD物质的状态方程（EOS）仍然是一个主要挑战。随着来自电磁和重力波观测的中子恒星质量，半径和潮汐变形的测量的进步，中子恒星在约束超密集的QCD物质EOS方面起着重要作用。在这项工作中，我们提出了一种新颖的方法，该方法利用了深度学习技术来重建来自Mass-Radius（M-R）观测值的中子星EOS。我们采用神经网络（NNS）以$ \ sim $ \ sim $ 1-7倍的核饱和密度的范围内代表EOS。实施了无监督的自动分化（AD）框架以优化EOS，以便通过TOV方程屈服，这是最适合观测值的M-R曲线。我们证明了该方法通过在模拟数据（即从随机生成的多环反应EOS）中重建EOS来起作用。重建后的EOS仅使用11个模拟M-R对观测值，以合理的精度拟合模拟数据，接近当前实际观察次数。
The Equation of State (EoS) of strongly interacting cold and hot ultra-dense QCD matter remains a major challenge in the field of nuclear astrophysics. With the advancements in measurements of neutron star masses, radii, and tidal deformabilities, from electromagnetic and gravitational wave observations, neutron stars play an important role in constraining the ultra-dense QCD matter EoS. In this work, we present a novel method that exploits deep learning techniques to reconstruct the neutron star EoS from mass-radius (M-R) observations. We employ neural networks (NNs) to represent the EoS in a model-independent way, within the range of $\sim$1-7 times the nuclear saturation density. The unsupervised Automatic Differentiation (AD) framework is implemented to optimize the EoS, so as to yield through TOV equations, an M-R curve that best fits the observations. We demonstrate that this method works by rebuilding the EoS on mock data, i.e., mass-radius pairs derived from a randomly generated polytropic EoS. The reconstructed EoS fits the mock data with reasonable accuracy, using just 11 mock M-R pairs observations, close to the current number of actual observations.
