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arXiv 1910.05554nucl-th
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从中子星性质揭示核物质EoS:一种有监督的机器学习方法

Unveiling the nuclear matter EoS from neutron star properties: a supervised machine learning approach

Márcio Ferreira, Constança Providência

摘要:

本文研究了有监督的机器学习方法在提取中子星观测量与核物质状态方程之间的非线性映射中的应用。使用泰勒展开饱和密度,我们已经产生了一组模型独立的EoS描述恒星物质的约束下的核物质参数是一致的,因果关系,并与天体物理观测一致。从这个集合中,使用两种不同的机器学习方法来学习NS潮汐变形性和半径对核物质参数的完全非线性依赖性。由于学习的非线性映射的高精度,我们能够分析每个核物质参数对NS观测值的影响,识别对EoS属性的依赖性超出线性相关性,并预测哪些恒星允许我们绘制强约束。

英文摘要:

We explore supervised machine learning methods in extracting the non-linear maps between neutron stars (NS) observables and the equation of state (EoS) of nuclear matter. Using a Taylor expansion around saturation density, we have generated a set of model independent EoS describing stellar matter constrained by nuclear matter parameters that are thermodynamically consistent, causal, and consistent with astrophysical observations. From this set, the full non-linear dependencies of the NS tidal deformability and radius on the nuclear matter parameters were learned using two distinct machine learning methods. Due to the high accuracy of the learned non-linear maps, we were able to analyze the impact of each nuclear matter parameter on the NS observables, identify dependencies on the EoS properties beyond linear correlations and predict which stars allow us to draw strong constraints.

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。

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