It is of great interest to understand the equation of state (EOS) of the neutron star (NS), whose core includes highly dense matter. However, there are large uncertainties in the theoretical predictions for the EOS of NS. It is useful to develop a new framework, which is flexible enough to consider the systematic error in theoretical predictions and to use them as a best guess at the same time. We employ a deep neural network to perform a non-parametric fit of the EOS of NS using currently available data. In this framework, the Gaussian process is applied to represent the EOSs and the training set data required to close physical solutions. Our model is constructed under the assumption that the true EOS of NS is a perturbation of the relativistic mean-field model prediction. We fit the EOSs of NS using two different example datasets, which can satisfy the latest constraints from the massive neutron stars, NICER, and the gravitational wave of the binary neutron stars. Given our assumptions, we find that a maximum neutron star mass is $2.38^{+0.15}_{-0.13} M_\odot$ or $2.41^{+0.15}_{-0.14}$ at $95\%$ confidence level from two different example datasets. It implies that the $1.4 M_\odot$ radius is $12.31^{+0.29}_{-0.31}$ km or $12.30^{+0.35}_{-0.37}$ km. These results are consistent with results from previous studies using similar priors. It has demonstrated the recovery of the EOS of NS using a nonparametric model.
Astrophysical constraints on neutron star $f$-modes with a nonparametric equation of state representation
National Institute of Technology, Rourkela（鲁尔基拉国家理工学院）；Canadian Institute for Theoretical Astrophysics, University of Toronto（多伦多大学加拿大理论天体物理研究所）；Department of Physics, University of Toronto（多伦多大学物理系）；INFN Sezione di Catania, Dipartimento di Fisica（意大利国家核物理研究院卡塔尼亚分部物理系）；Perimeter Institute for Theoretical Physics（珀米特理论物理研究所）
Constrained Gaussian-process bridge prior for neutron-star equation-of-state inference
Center for Cosmology and AstroParticle Physics (CCAPP), Ohio State University（俄亥俄州立大学宇宙学与天体粒子物理中心）；Department of Physics, The Ohio State University（俄亥俄州立大学物理系）；Institut für Theoretische Physik, Goethe Universität（法兰克福歌德大学理论物理研究所）；Faculty of Science and Technology, University of Stavanger（斯塔万格大学科学技术学院）
本文提出了一种生成模型无关、非参数先验的方法，用于中子星方程 of state推断，通过构造具有全局热力学约束的高斯过程桥，确保稳定性、因果性和热力学一致性。
Neutron structure function $F_2^n(x)$ from deep inelastic electron scattering off few-nucleon systems
研究了从氘核、$^{3}He$和$^{3}H$的深部非弹性结构函数的联合测量中可靠地提取$ x < 0.85$的中子深部非弹性结构函数$F_2^n(x)$的可能性。如果适当考虑原子核结构的影响，这种提取中的模型依赖性，与原子核中核子之间可能的不同相互作用有关，被证明是弱的。提出了一个联合分析的深非弹性结构函数的这些核研究的影响超出了脉冲近似。
