论文标题
深度学习和重力波种群的贝叶斯推断：等级黑洞合并
Deep learning and Bayesian inference of gravitational-wave populations: Hierarchical black-hole mergers
论文作者
论文摘要
引力波事件的目录正在增长，我们希望通过推断质量和旋转的分布来限制恒星质量黑洞合并的基本天体物理学的希望。尽管常规分析通过简单的现象学模型来分析该人群，但我们提出了一种基于仿真的方法，可以将天体物理模拟与重力波数据进行比较。我们将最新的深度学习技术与分层贝叶斯推论相结合，并利用我们在最新的Ligo/Wirgo目录中从重力波事件中限制重复黑洞合并的特性的方法。深度神经网络使我们能够（i）构建一个灵活的单渠道种群模型，该模型准确地模拟了层次合并的简单参数化数值模拟，（ii）估计选择效果，以及（iii）恢复重复合并世代的分支比率。在我们的结果中，我们找到了以下内容：主机环境的逃逸速度的分布有利于小于$ 100〜 \ MATHRM {km \，s^{ - 1}} $的价值，但相对平坦，大约37 \％的第一代合并保留在其主机环境中；第一代黑洞天生具有最大质量，与成对稳定超新星的当前估计值兼容。质量和自旋分布都有多模式的子结构，在我们的模型中可以通过重复合并来解释。和较高的组成部分的二进制文件至少占基础人口的$ 14 \％。尽管这些结果是通过模仿简化模型来推断的，但我们提出的深入学习管道很容易适用于现实的天体物理模拟
The catalog of gravitational-wave events is growing, and so are our hopes of constraining the underlying astrophysics of stellar-mass black-hole mergers by inferring the distributions of, e.g., masses and spins. While conventional analyses parametrize this population with simple phenomenological models, we propose an emulation-based approach that can compare astrophysical simulations against gravitational-wave data. We combine state-of-the-art deep-learning techniques with hierarchical Bayesian inference and exploit our approach to constrain the properties of repeated black-hole mergers from the gravitational-wave events in the most recent LIGO/Virgo catalog. Deep neural networks allow us to (i) construct a flexible single-channel population model that accurately emulates simple parametrized numerical simulations of hierarchical mergers, (ii) estimate selection effects, and (iii) recover the branching ratios of repeated-merger generations. Among our results, we find the following: The distribution of host-environment escape speeds favors values less than $100~\mathrm{km\,s^{-1}}$ but is relatively flat, with around $37\%$ of first-generation mergers retained in their host environments; first-generation black holes are born with a maximum mass that is compatible with current estimates from pair-instability supernovae; there is multimodal substructure in both the mass and spin distributions, which, in our model, can be explained by repeated mergers; and binaries with a higher-generation component make up at least $14\%$ of the underlying population. Though these results are inferred through emulation of a simplified model, the deep-learning pipeline we present is readily applicable to realistic astrophysical simulations
