论文标题
使用变异自动编码器的中子星方程的非参数表示状态
Nonparametric Representation of Neutron Star Equation of State Using Variational Autoencoder
论文作者
论文摘要
我们通过使用变分自动编码器（VAE）引入了状态恒星方程（EOS）的新的非参数表示。作为深度神经网络，VAE经常用于降低维度，因为它可以使用Encoder组件将输入数据压缩到低维的潜在空间，然后使用解码器组件重建数据。一旦训练了VAE，就可以将VAE的解码器作为发电机。我们使用基于\ citet {2021apj ... 919 ... 11H}的非参数表示方法生成的100,000个EOSS作为训练集，并尝试使用神经网络的不同设置，然后我们将获得一个带有四个参数的EOS生成器（训练有素的VAE的解码器）。我们使用二进制中子星（BNS）合并事件GW170817的质量\ textendash {}潮汐可启示性数据，质量\ textendendash {} psr j0030+0451的半径数据贝叶斯推断。包括所有观测值的分析的总体结果为$ r_ {1.4} = 12.59^{+0.36} _ { - 0.42} \，\ rm km $，$λ_{1.4} = 489^{+114} max} = 2.20^{+0.37} _ { - 0.19} \，\ rm m_ \ odot $（$ 90 \％$可信级别），其中$ r_ {1.4} $/$ c/$ c/$λ_{1.4} $是radius/tidal-tidal-tidal-tidal-deformability $ 1.4 \ oc n y__4 \ oc cod，cod， $ m _ {\ rm max} $是非旋转ns的最大质量。结果表明，与原始方法相比，VAE技术的实现可以获得合理的结果，而加速计算$ \ sim $ 3 \ textendash10或更多。
We introduce a new nonparametric representation of the neutron star (NS) equation of state (EoS) by using the variational autoencoder (VAE). As a deep neural network, the VAE is frequently used for dimensionality reduction since it can compress input data to a low-dimensional latent space using the encoder component and then reconstruct the data using the decoder component. Once a VAE is trained, one can take the decoder of the VAE as a generator. We employ 100,000 EoSs that are generated using the nonparametric representation method based on \citet{2021ApJ...919...11H} as the training set and try different settings of the neural network, then we get an EoS generator (trained VAE's decoder) with four parameters. We use the mass\textendash{}tidal-deformability data of binary neutron star (BNS) merger event GW170817, the mass\textendash{}radius data of PSR J0030+0451, PSR J0740+6620, PSR J0437-4715, and 4U 1702-429, and the nuclear constraints to perform the joint Bayesian inference. The overall results of the analysis that includes all the observations are $R_{1.4}=12.59^{+0.36}_{-0.42}\,\rm km$, $Λ_{1.4}=489^{+114}_{-110}$, and $M_{\rm max}=2.20^{+0.37}_{-0.19}\,\rm M_\odot$ ($90\%$ credible levels), where $R_{1.4}$/$Λ_{1.4}$ are the radius/tidal-deformability of a canonical $1.4\,\rm M_\odot$ NS, and $M_{\rm max}$ is the maximum mass of a non-rotating NS. The results indicate that the implementation of the VAE techniques can obtain the reasonable results, while accelerate calculation by a factor of $\sim$ 3\textendash10 or more, compared with the original method.
