Rapid model comparison of equations of state from gravitational wave observation of binary neutron star coalescences
Rapid model comparison of equations of state from gravitational wave observation of binary neutron star coalescences
复制标题
DOI：
10.1103/physrevd.104.083003
复制
发表时间：
2021-04
影响因子：
5
通讯作者：
Shaon Ghosh;Xiaoshu Liu;J. Creighton;I. M. Hernandez;W. Kastaun;G. Pratten
中科院分区：
文献类型：
--
作者：
Shaon Ghosh;Xiaoshu Liu;J. Creighton;I. M. Hernandez;W. Kastaun;G. Pratten
双星中子星星GW 170817的发现是引力波天文学领域的一个分水岭。在我们能够从这个发现中发现的丰富多样的信息中，有第一个中子星星半径的非电磁测量，以及冷核状态方程。它还导致了一个大型的状态方程模型选择研究重力波数据。在这些研究中，对每个候选状态方程模型进行贝叶斯嵌套抽样运行，以计算它们在引力波数据中的证据。这样的研究，虽然是无价的，但在计算上是昂贵的，并且对于任何新的模型都需要重复的、冗余的计算。我们提出了一种新的技术进行模型选择的状态方程在一个非常快速的方式（10分钟）在任何任意模型。我们测试这种技术对嵌套采样模型选择技术的结果发表的LIGO/Virgo合作，并表明，结果是在良好的协议与贝叶斯因子的中位数分数误差约为10%，其中我们假设，真正的贝叶斯因子计算在上述嵌套采样运行。我们发现，最高的分数误差发生在状态方程模型中，这些模型在后验分布中的支持度非常小，从而导致较大的统计不确定性。然后，我们使用这种方法结合联合收割机多个双中子星星合并计算状态方程模型之间的联合贝叶斯因子。这是通过堆叠单个事件的证据并从这些堆叠的证据中计算每对状态方程的贝叶斯因子来实现的。
The discovery of the coalescence of binary neutron star GW170817 was a watershed moment in the field of gravitational wave astronomy. Among the rich variety of information that we were able to uncover from this discovery was the first non-electromagnetic measurement of the neutron star radius, and the cold nuclear equation of state. It also led to a large equation of state model selection study from gravitational-wave data. In those studies Bayesian nested sampling runs were conducted for each candidate equation of state model to compute their evidence in the gravitational-wave data. Such studies, though invaluable, are computationally expensive and require repeated, redundant, computation for any new models. We present a novel technique to conduct model selection of equation of state in an extremely rapid fashion (∼minutes) on any arbitrary model. We test this technique against the results of a nested-sampling model selection technique published earlier by the LIGO/Virgo collaboration, and show that the results are in good agreement with a median fractional error in Bayes factor of about 10%, where we assume that the true Bayes factor is calculated in the aforementioned nested sampling runs. We found that the highest fractional error occurs for equation of state models that have very little support in the posterior distribution, thus resulting in large statistical uncertainty. We then used this method to combine multiple binary neutron star mergers to compute a joint-Bayes factor between equation of state models. This is achieved by stacking the evidence of the individual events and computing the Bayes factor from these stacked evidences for each pairs of equation of state.