Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
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DOI：
10.1038/s41567-021-01425-7
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发表时间：
2019-09
期刊：
影响因子：
19.6
通讯作者：
H. Gabbard;C. Messenger;I. Heng;F. Tonolini;R. Murray-Smith
中科院分区：
文献类型：
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作者：
H. Gabbard;C. Messenger;I. Heng;F. Tonolini;R. Murray-Smith
随着全球引力波探测器网络的灵敏度不断提高，我们预计每年将观测到数百个瞬态引力波事件。目前用于估计其源参数的方法采用最佳敏感但计算成本高的贝叶斯推理方法，其中典型的分析在6 h和6 d之间进行。对于双星中子星星和中子星-黑洞系统，预计在1秒和1分钟之间的时间尺度上会出现相应的电磁特征。然而，目前最快的方法，提醒电磁后续观察员可以提供估计的顺序1分钟的范围有限的关键源参数。在这里，我们证明了在二进制黑洞信号上预训练的条件变分自动编码器可以返回贝叶斯后验概率估计。对于给定的先验参数空间，训练过程只需要执行一次，然后得到的训练机器可以比现有技术快六个数量级生成描述后验分布的样本。
With the improving sensitivity of the global network of gravitational-wave detectors, we expect to observe hundreds of transient gravitational-wave events per year. The current methods used to estimate their source parameters employ optimally sensitive but computationally costly Bayesian inference approaches, where typical analyses have taken between 6 h and 6 d. For binary neutron star and neutron star–black hole systems prompt counterpart electromagnetic signatures are expected on timescales between 1 s and 1 min. However, the current fastest method for alerting electromagnetic follow-up observers can provide estimates in of the order of 1 min on a limited range of key source parameters. Here, we show that a conditional variational autoencoder pretrained on binary black hole signals can return Bayesian posterior probability estimates. The training procedure need only be performed once for a given prior parameter space and the resulting trained machine can then generate samples describing the posterior distribution around six orders of magnitude faster than existing techniques.