Constraining the primordial black hole scenario with Bayesian inference and machine learning: The GWTC-2 gravitational wave catalog
Constraining the primordial black hole scenario with Bayesian inference and machine learning: The GWTC-2 gravitational wave catalog
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DOI：
10.1103/physrevd.103.023026
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发表时间：
2020-11
影响因子：
5
通讯作者：
Ka-wah Wong;G. Franciolini;V. De Luca;V. Baibhav;E. Berti;P. Pani;A. Riotto
中科院分区：
文献类型：
--
作者：
Ka-wah Wong;G. Franciolini;V. De Luca;V. Baibhav;E. Berti;P. Pani;A. Riotto
原始黑洞（PBH）可能在早期宇宙中形成，并且可能至少包含一部分暗物质。使用最近发布的 LIGO-Virgo 合作第三次观测运行的 GWTC-2 数据集，我们研究了当前的观测结果是否与迄今为止检测到的所有黑洞合并都具有原始起源的假设相一致。我们将 PBH 形成模型限制在基于深度学习技术的分层贝叶斯推理框架内，找到这些模型的独特特征的最佳拟合值，包括 PBH 初始质量函数、暗物质中 PBH 的比例以及吸积效率。 GWTC-2 数据集中存在多个旋转双星，有利于 PBH 累积和旋转的情况。我们的结果表明，PBH 可能仅占暗物质总量的 0.3% 以下，并且预测的 PBH 丰度仍然与其他约束兼容。
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.