发表机构
University of Florida; Institute of Cosmology and Gravitation, University of Portsmouth（佛罗里达大学; 朴茨茅斯宇宙学与引力研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出PopCWB，一种基于cWB的前向贝叶斯框架，用于推断双黑洞总体参数，并应用于O3数据，约束了最大黑洞质量和并合率。
AI 中文摘要
我们提出了PopCWB，一个基于非建模搜索管线coherent WaveBurst (cWB)的双黑洞（BBH）总体推断的贝叶斯框架。标准的总体分析采用逆向方法，通过使用单个事件的后验样本来推断给定检测事件集合的底层总体模型参数。在PopCWB中，我们采用前向方法进行总体推断，通过变化总体模型来识别描述观测事件的最优模型。PopCWB不使用事件级后验样本，而是使用由cWB重建的大量模拟事件及其通过机器学习回归估计的总质量来构建边际概率分布。这些边际概率随后用于构建似然函数。我们将PopCWB应用于LIGO--Virgo--KAGRA探测器第三次观测运行（O3）期间由cWB检测到的BBH事件。我们约束了一个天体物理动机的BBH总体模型，该模型包含了脉动对不稳定超新星和动力学并合的影响。分析预测了来自恒星演化的最大黑洞质量为$\mathbf{45.5^{+5.3}_{-6.5} M_{\odot}}$，并推断出局部BBH并合率为$\mathbf{23.1^{+11.3}_{-8.4} \\, Gpc^{-3} yr^{-1}}$。我们将这些结果与文献中现有的总体模型进行了比较。
英文摘要
We present PopCWB, a Bayesian framework for the inference of the binary black hole (BBH) population based on the unmodeled search pipeline coherent WaveBurst (cWB). The standard population analysis takes an inverse approach by inferring the underlying population model parameters for a given set of detected events using the individual posterior samples. In PopCWB, we take a forward approach to population inference by varying population models to identify the most optimal one which describes the observed events. Rather than using event-level posterior samples, PopCWB uses a large set of simulated events reconstructed by cWB and their total mass, estimated with a machine-learning regression, to construct marginal probability distributions. These marginal probabilities are then used in the construction of the likelihood. We apply PopCWB to BBH events detected by cWB during the third observing run (O3) of the LIGO--Virgo--KAGRA detectors. We constrain an astrophysically motivated BBH population model that incorporates the effects of pulsational pair-instability supernovae and dynamical mergers. The analysis predicts a maximum black hole mass of $\mathbf{45.5^{+5.3}_{-6.5} M_{\odot}}$ from stellar evolution and an inferred local BBH merger rate of $\mathbf{23.1^{+11.3}_{-8.4} \, Gpc^{-3} yr^{-1}}$. We compare these results with the existing population models from the literature.
Comments10 pages, 6 figures
