中子星中暗物质的可行性：一项定量分析
Feasibility of Dark Matter in Neutron Stars: A Quantitative Analysis
AI总结：
本研究通过相对论平均场等模型定量分析暗物质对中子星性质的影响，结合引力波与X射线观测数据修正模型，验证C-Love关系的有效性，还探究了sigma-cut势和星震振荡模式的作用，为理解中子星内部与暗物质提供新依据。
摘要：
本论文研究了暗物质对中子星性质的影响，重点关注质量、半径和潮汐形变能力。研究采用双流体和单流体模型，通过相对论平均场（Relativistic Mean Field, RMF）方法将暗物质纳入物态方程（equation of state, EOS）。研究发现，暗物质含量增加会降低中子星的最大质量、半径和潮汐形变能力。贝叶斯推断结合LIGO-Virgo引力波数据与NICER质量-半径测量结果，对这些模型进行了修正。尽管存在暗物质的影响，半普适的C-Love关系仍然成立。机器学习技术可有效分类含暗物质的混合中子星。论文还探究了物态方程中的sigma-cut势，该势会在高密度下使物态方程变硬，从而支持更大的半径和更低的f模频率。对非径向振荡（尤其是f模和p模）的研究凸显了它们对中子星组成和物态方程的敏感性。这些发现增进了我们对中子星内部结构及暗物质作用的理解，强调了进一步推进观测与理论研究的必要性。
英文摘要：
This thesis investigates the impact of dark matter on neutron star properties, focusing on mass, radius, and tidal deformability. Using two-fluid and single-fluid models, dark matter is incorporated into the equation of state (EOS) via a Relativistic Mean Field (RMF) approach. The study finds that increasing dark matter content reduces the maximum mass, radius, and tidal deformability. Bayesian inference, supported by LIGO-Virgo gravitational wave data and NICER mass-radius measurements, refines these models. Despite dark matter's influence, the semi-universal C-Love relation remains valid. Machine learning techniques effectively classify dark matter-admixed neutron stars. The thesis also explores a sigma-cut potential in the EOS, which stiffens the EOS at high densities, favoring larger radii and lower f-mode frequencies. The study of non-radial oscillations, particularly f- and p-modes, highlights their sensitivity to neutron star composition and EOS. These findings enhance our understanding of neutron star interiors and dark matter's role, emphasizing the need for further observational and theoretical advancements.
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。
