Brendan T. Reed, Rahul Somasundaram, Soumi De, Cassandra L. Armstrong, Pablo Giuliani, Collin Capano, Duncan A. Brown and Ingo Tews
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引用次数: 0
摘要
对双中子星(BNS)合并的引力波观测有可能彻底改变我们对核状态方程(EOS)以及决定其性质的基本相互作用的理解。然而,贝叶斯参数估计框架通常不会对决定 EOS 的微观核物理参数进行采样。这样做的主要障碍之一是求解中子星结构方程(即托尔曼-奥本海默-沃尔科夫(Tolman-Oppenheimer-Volkoff,TOV)方程)所涉及的计算成本。在本文中,我们探索了模拟 TOV 方程求解的方法:多层感知器 (MLP)、高斯过程和还原基方法 (RBM) 的数据驱动变体。我们针对核 EOS 的三种不同参数化实现了这些仿真器,每种仿真器的复杂程度不同,由模型参数的数量表示。我们发现,基于 MLP 的模拟器通常比其他两种算法更精确,而相对于完整的高保真 TOV 求解器,RBM 的求解速度最快。我们利用这些仿真器对潜在的高噪声 BNS 观测结果进行了简单的参数推断,结果表明我们的仿真器预测的后验结果与完整 TOV 求解器预测的后验结果非常一致。
Toward Accelerated Nuclear-physics Parameter Estimation from Binary Neutron Star Mergers: Emulators for the Tolman–Oppenheimer–Volkoff Equations
Gravitational-wave observations of binary neutron-star (BNS) mergers have the potential to revolutionize our understanding of the nuclear equation of state (EOS) and the fundamental interactions that determine its properties. However, Bayesian parameter estimation frameworks do not typically sample over microscopic nuclear-physics parameters that determine the EOS. One of the major hurdles in doing so is the computational cost involved in solving the neutron-star structure equations, known as the Tolman–Oppenheimer–Volkoff (TOV) equations. In this paper, we explore approaches to emulating solutions for the TOV equations: multilayer perceptrons (MLPs), Gaussian processes, and a data-driven variant of the reduced basis method (RBM). We implement these emulators for three different parameterizations of the nuclear EOS, each with a different degree of complexity represented by the number of model parameters. We find that our MLP-based emulators are generally more accurate than the other two algorithms, whereas the RBM results in the largest speedup with respect to the full high-fidelity TOV solver. We employ these emulators for a simple parameter inference using a potentially loud BNS observation and show that the posteriors predicted by our emulators are in excellent agreement with those obtained from the full TOV solver.