论文标题
鉴于更好的精确质量 - 拉迪乌斯测量值
Bosonic Dark Matter in Light of the NICER Precise Mass-Radius Measurements
论文作者
论文摘要
我们鉴于中子星内部组成探索器(NICER)和Ligo/Pirgo探测器的最新多通间观察,我们探索了中子星(NSS)中自我相互作用的骨骼暗物质(DM)的存在。骨气DM分布成NS内部或周围的光环,从而形成了DM混合NS。由于DM模型参数和分数,我们专注于混合物体的可见和深色半径的变化。结果表明,DM核心的形成减少了可见的半径,总质量将它们推到观察限制以下,而Halo形成则有利于最新的Mass-Radius观测值。此外,我们通过应用半径,最大质量和潮汐变形性约束,考虑了两个状态的核物质方程式的骨气DM模型的参数空间。我们的研究允许排除一系列DM级分,自耦合常数和子GEV玻色子质量,这将累积的DM量限制为相对较低的值,以与天体物理界限一致。在本文中,我们介绍了与DM混合NS相对应的脉冲曲线的主要特征,这是一种新颖的可观察数量。我们发现,脉冲曲线中最小通量的深度至关重要取决于NS周围的DM量及其紧凑性。当前/未来的天体物理学任务可能会测试NSS内DM存在的可能性,并通过多次观察破坏不同情况之间的脱模效。
We explore the presence of self-interacting bosonic dark matter (DM) within neutron stars (NSs) in light of the latest multi-messenger observations of the Neutron Star Interior Composition Explorer (NICER) and LIGO/Virgo detectors. The bosonic DM is distributed as a core inside the NS or as a halo around it leading to formation of a DM admixed NS. We focus on the variation of the visible and dark radius of the mixed object due to DM model parameters and fractions. It is shown that DM core formation reduces the visible radius and the total mass pushing them below observational limits while halo formation is in favor of the latest mass-radius observations. Moreover, we scan over the parameter space of the bosonic DM model considering two nuclear matter equation of states by applying the radius, maximum mass and tidal deformability constraints. Our investigation allows for the exclusion of a range of DM fractions, self-coupling constant and sub-GeV boson masses, which limits the amount of accumulated DM to relatively low values to be consistent with astrophysical bounds. In this paper, we introduce main features of the pulse profile corresponding to the DM admixed NS as a novel observable quantity. We find that the depth of minimum fluxes in the pulse profiles crucially depends on the amount of DM around NS and its compactness. The current/future astrophysics missions may test the possibility of the existence of DM within NSs and break the degeneracies between different scenarios via multiple observations.