使用条件变分自编码器对微透镜引力波进行参数估计
Parameter estimation of microlensed gravitational waves with Conditional Variational Autoencoders
AI总结：
本文提出使用条件变分自编码器（CVAE）估计微透镜双黑洞引力波波形参数，相比贝叶斯方法Bilby推理速度最高快五个数量级，将CVAE先验纳入Bilby可减少约48%运行时间且不损失精度，为未来低延迟透镜信号搜索提供了有前途的工具。
摘要：
引力波（GWs）的引力透镜效应为在多个尺度上研究宇宙学和天体物理学提供了独特的机会。特别是，由于传统贝叶斯推断的高计算成本，探测微透镜特征需要高效的参数估计方法。在本文中，我们探索了深度学习的使用，即条件变分自编码器（CVAE），来估计微透镜双黑洞（模拟）波形的参数。我们发现，与 Bilby 等贝叶斯方法相比，我们的 CVAE 模型产生了准确的参数估计并显著节省了计算成本（推理速度最高快五个数量级）。此外，基于透镜参数的 CVAE 后验的 95% 置信区间，将 CVAE 生成的先验纳入 Bilby，使 Bilby 的平均运行时间减少了约 48%，且准确度没有任何损失。我们的结果表明，CVAE 模型是未来低延迟搜索透镜信号的有前途的工具。对实际信号的进一步应用以及与高级流水线的集成，有助于扩展引力波天文台在探测微透镜事件方面的能力。
英文摘要：
Gravitational lensing of gravitational waves (GWs) provides a unique opportunity to study cosmology and astrophysics at multiple scales. Detecting microlensing signatures, in particular, requires efficient parameter estimation methods due to the high computational cost of traditional Bayesian inference. In this paper we explore the use of deep learning, namely Conditional Variational Autoencoders (CVAE), to estimate parameters of microlensed binary black hole (simulated) waveforms. We find that our CVAE model yields accurate parameter estimation and significant computational savings compared to Bayesian methods such as Bilby (up to five orders of magnitude faster inferences). Moreover, the incorporation of CVAE-generated priors into Bilby, based on the 95% confidence intervals of the CVAE posterior for the lensing parameters, reduces Bilby's average runtime by around 48% without any penalty on accuracy. Our results suggest that a CVAE model is a promising tool for future low-latency searches of lensed signals. Further applications to actual signals and integration with advanced pipelines could help extend the capabilities of GW observatories in detecting microlensing events.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
