论文标题
夸克之星的结构：基于贝叶斯推断和神经网络建模的比较分析
Structure of Quark Star: A Comparative Analysis of Bayesian Inference and Neural Network Based Modeling
论文作者
论文摘要
在这项工作中，我们比较了两种强大的参数估计方法，即贝叶斯推理和基于神经网络的学习，以研究状态的夸克物质方程，并以声音参数的持续速度和在两户家庭场景中的夸克恒星的结构。我们使用来自多个X射线源的质量和半径估计，还使用引力波事件的质量和潮汐变形性测量来限制我们模型的参数。从两种方法中发现的结果是一致的。预测的声音速度与保形极限兼容。
In this work, we compare two powerful parameter estimation methods namely Bayesian inference and Neural Network based learning to study the quark matter equation of state with constant speed of sound parametrization and the structure of the quark stars within the two-family scenario. We use the mass and radius estimations from several X-ray sources and also the mass and tidal deformability measurements from gravitational wave events to constrain the parameters of our model. The results found from the two methods are consistent. The predicted speed of sound is compatible with the conformal limit.
