一种几何物理引导的机器学习推理方法用于中子星最大质量及逆问题
A geometric physics-informed machine learning inference for the neutron star maximum mass and the inverse problem
AI总结：
本研究通过物理引导的机器学习方法，利用几何参数预测中子星最大质量和半径，并揭示声速峰值导致的相变机制。
摘要：
中子星最重质量和黑洞最轻质量之间的明显质量边界仍存在疑问。这是由于我们对超核密度下物质性质的无知所导致的，这些密度存在于中子星的核心中。本研究采用物理引导的机器学习方法来解决这些问题，受天体物理学观测的指导。Transformer模型在无偏生成的状态方程集合上进行训练。在中子星的质量半径序列上定义了两个几何参数——前弯曲和后弯曲。Transformer提供了一个两步解决方案：首先，模型利用弯曲参数预测最大质量和半径。其次，它预测声速剖面的平方，完成逆映射。预测表明，当声速在低密度时达到峰值时，会形成大质量中子星，导致强后弯曲和早期相变到夸克物质。大质量恒星在低密度时更倾向于刚性状态方程，且恒星核心的物质密度足够低。根据天体物理学约束的Transformer模型预测，中子星的最大质量为2.477个太阳质量，1.4个太阳质量的中子星的最小半径约为11.498公里。
英文摘要：
The existence of a distinct mass boundary between the heaviest neutron stars and the lightest black holes remains in question. It is an artefact of our ignorance of the properties of matter at supra-nuclear densities, which exist in the cores of neutron stars. The study addresses these problems with a physics-informed machine learning approach, guided by astrophysical observations. The Transformer model is trained on an agnostically generated ensemble of equations of state. Two geometric parameters are defined on the mass-radius sequence of a neutron star--the front bending and the back bending. The transformer provides a two-step solution: first, the model predicts the maximum mass and radius using the bending parameters. Second, it predicts the square of the sound speed profile, completing the inverse mapping. The prediction is that massive neutron stars form when the sound speed peaks at low density, leading to strong back-bending and an early phase transition to quark matter. Massive stars favour a stiff equation of state at low density, and the density of matter at the star's core is sufficiently small. The maximum mass for a neutron star predicted by the astrophysical constrained transformer model is $2.477$ solar masses, and a minimum radius of about $11.498$ km for a neutron star of $1.4$ solar masses.
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