中子星状态方程唯象参数模型中的隐式关联
Implicit correlations within phenomenological parametric models of the neutron star equation of state
摘要：
近年来，对中子星天体物理探测的数量和精度的迅速提高，使中子星状态方程的推导成为可能。观测的目标是中子星的不同宏观性质，这些性质因星而异，例如质量和半径，但状态方程允许对所有中子星进行共同描述。为了将这些观测联系起来，同时推断致密物质和中子星的性质，引入了状态方程模型。参数模型依赖于精心设计的功能形式，再现了大量的现实状态方程。这种模型受益于其简单性，但也有局限性，因为任何有限参数模型都不能精确地近似所有可能的状态方程。非参数模型通过增加模型自由度来克服这一点，但代价是增加了复杂性。在这项研究中，我们比较常见的参数和非参数模型，量化前者的局限性，并研究我们目前对高密度物理的理解建模的影响。我们发现，参数模型施加强烈的依赖模型，有时不透明，密度尺度之间的相关性。这种密度间的相关性导致了更严格的约束，而这些约束是没有数据支持的，并可能导致对状态方程和单个中子星性质的有偏见的推断。
英文摘要：
The rapid increase in the number and precision of astrophysical probes of neutron stars in recent years allows for the inference of their equation of state. Observations target different macroscopic properties of neutron stars which vary from star to star, such as mass and radius, but the equation of state allows for a common description of all neutron stars. To connect these observations and infer the properties of dense matter and neutron stars simultaneously, models for the equation of state are introduced. Parametric models rely on carefully engineered functional forms that reproduce a large array of realistic equations of state. Such models benefit from their simplicity but are limited because any finite-parameter model cannot accurately approximate all possible equations of state. Nonparametric models overcome this by increasing model freedom at the cost of increased complexity. In this study, we compare common parametric and nonparametric models, quantify the limitations of the former, and study the impact of modeling on our current understanding of high-density physics. We show that parametric models impose strongly model-dependent, and sometimes opaque, correlations between density scales. Such interdensity correlations result in tighter constraints that are unsupported by data and can lead to biased inference of the equation of state and of individual neutron star properties.
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