论文标题
使用条件变异自动编码器的全天空连续重力搜索搜索的快速参数估计
Rapid parameter estimation for an all-sky continuous gravitational wave search using conditional varitational auto-encoders
论文作者
论文摘要
对连续重力波的全天搜索通常是模型依赖性的，并且在计算上昂贵的运行。相比之下，SOAP是一种模型不足的搜索，可快速返回时频平面中的候选信号轨道。在这项工作中，我们扩展了肥皂搜索，以在特定信号模型的天体物理参数上返回宽阔的贝叶斯后期。这些约束大大减少了任何后续搜索需要探索的参数空间的体积，因此可以提高鉴定和确认候选者的速度。我们的方法使用机器学习技术，特别是有条件的变异自动编码器，并对连续波信号的四个多普勒参数的后验分布进行快速估计。它这样做的情况不需要明确的可能性功能，也可以在训练中显示任何真正的贝叶斯后代。我们演示了如何减少多普勒参数空间量的$ \ MATHCAL {O}（10^{ - 7}）$的$ \ MATHCAL卷，用于SNR 100的信号。
All-sky searches for continuous gravitational waves are generally model dependent and computationally costly to run. By contrast, SOAP is a model-agnostic search that rapidly returns candidate signal tracks in the time-frequency plane. In this work we extend the SOAP search to return broad Bayesian posteriors on the astrophysical parameters of a specific signal model. These constraints drastically reduce the volume of parameter space that any follow-up search needs to explore, so increasing the speed at which candidates can be identified and confirmed. Our method uses a machine learning technique, specifically a conditional variational auto-encoder, and delivers a rapid estimation of the posterior distribution of the four Doppler parameters of a continuous wave signal. It does so without requiring a clear definition of a likelihood function, or being shown any true Bayesian posteriors in training. We demonstrate how the Doppler parameter space volume can be reduced by a factor of $\mathcal{O}(10^{-7})$ for signals of SNR 100.
