论文标题
用贝叶斯推理和机器学习来限制原始黑洞方案：GWTC-2重力波目录
Constraining the primordial black hole scenario with Bayesian inference and machine learning: the GWTC-2 gravitational wave catalog
论文作者
论文摘要
原始的黑洞（PBH）可能在早期的宇宙中形成，并且至少占暗物质的一小部分。利用最近发布的GWTC-2数据集从Ligo-Virgo协作的第三次观察运行中进行，我们研究了当前的观察结果是否与迄今为止检测到的所有黑洞合并均具有原始起源的假设。我们根据深度学习技术的层次结构贝叶斯推理框架来限制PBH形成模型，为这些模型的独特特征找到最佳拟合值，包括PBH初始质量功能，暗物质中的PBH的分数以及积聚效率。 GWTC-2数据集中有几种旋转二进制文件的存在有利于PBHS积聚和旋转的情况。我们的结果表明，PBHS可能仅包含小于$ 0.3 \％$的总暗物质的分数，并且预测的PBH丰度仍然与其他约束。
Primordial black holes (PBHs) might be formed in the early Universe and could comprise at least a fraction of the dark matter. Using the recently released GWTC-2 dataset from the third observing run of the LIGO-Virgo Collaboration, we investigate whether current observations are compatible with the hypothesis that all black hole mergers detected so far are of primordial origin. We constrain PBH formation models within a hierarchical Bayesian inference framework based on deep learning techniques, finding best-fit values for distinctive features of these models, including the PBH initial mass function, the fraction of PBHs in dark matter, and the accretion efficiency. The presence of several spinning binaries in the GWTC-2 dataset favors a scenario in which PBHs accrete and spin up. Our results indicate that PBHs may comprise only a fraction smaller than $0.3 \%$ of the total dark matter, and that the predicted PBH abundance is still compatible with other constraints.
