论文标题
中子星参数的神经网络重建状态的密集状态方程
Neural networks reconstruction of the dense-matter equation of state from neutron-star parameters
论文作者
论文摘要
目的：这项工作的目的是研究以自动编码器体系结构为指导的人工神经网络的应用，作为使用可观察的参数精确重建中子星形状态方程的方法：质量，半径和潮汐变形。此外，我们研究了仅使用重力波重建中子星形半径的效果，仅观察到潮汐变形性，即以直接方式相关的数量。方法：人工神经网络在状态重建方程中的应用利用了该机器学习模型的非线性潜力。由于网络中的每个神经元基本上都是一个非线性函数，因此可以在观测值和状态表的输出方程之间创建一个复杂的映射。在监督的训练范式中，我们在生成的数据集上构建了一些隐藏的层深神经网络，由中子星形壳的现实状态方程组成，与分段相对论的多层密集核心相连，具有代表状态的现实情况的参数。结果：我们证明了机器学习实施的性能，这些案例具有不同的观测和测量不确定性。此外，我们研究中子星质量分布对结果的影响。最后，我们使用基于现实的状态方程的模拟质量 - radius和质量 - 且且且且态度的序列测试了对参数多训练集训练的状态方程的重建。经过有限数据集训练的神经网络能够概括为现实模型之间的全局参数和状态输入表方程的映射。
Aims: The aim of this work is to study the application of the artificial neural networks guided by the autoencoder architecture as a method for precise reconstruction of the neutron star equation of state, using their observable parameters: masses, radii and tidal deformabilities. In addition we study how well the neutron star radius can be reconstructed using the gravitational-wave only observations of tidal deformability, i.e. quantities which are not related in a straightforward way. Methods: Application of artificial neural network in the equation of state reconstruction exploits the non-linear potential of this machine learning model. Since each neuron in the network is basically a non-linear function, it is possible to create a complex mapping between the input sets of observations and the output equation of state table. Within the supervised training paradigm, we construct a few hidden layer deep neural network on a generated data set, consisting of a realistic equation of state for the neutron star crust connected with a piecewise relativistic polytropes dense core, with parameters representative to the state-of-the art realistic equations of state. Results: We demonstrate the performance of our machine learning implementation with respect to the simulated cases with varying number of observations and measurement uncertainties. Furthermore we study the impact of the neutron star mass distributions on the results. Finally, we test the reconstruction of the equation of state trained on parametric polytropic training set using the simulated mass--radius and mass--tidal-deformability sequences based on realistic equations of state. Neural networks trained with a limited data set are able to generalize the mapping between global parameters and equation of state input tables for realistic models.
