$\texttt{BilbyFlow}$：面向引力波天文学的用户友好型神经后验估计工具
$\texttt{BilbyFlow}$: user-friendly neural posterior estimation for gravitational-wave astronomy
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中文总结 AI 辅助
本研究推出开源工具$\texttt{BilbyFlow}$，将神经后验估计整合入$\texttt{Bilby}$代码套件，分析GWTC-3的38个引力波事件，实现76%事件的重要性采样效率＞1%，中位效率7%，运行时间大幅缩短。
中文摘要 AI 辅助
贝叶斯推断是新兴引力波天文学领域的核心方法，但传统基于随机采样器的贝叶斯推断计算成本高昂，每个事件需耗时数小时至数天。下一代引力波天文台的事件率和信噪比将较当前一代显著提升，因此需要变革性技术来支撑相关科学研究。近期研究表明，神经后验估计（NPE）是极具潜力的技术路径：通过训练神经网络近似引力波参数的后验分布，可在远短于随机采样器的时间内生成后验样本。本研究介绍$\texttt{BilbyFlow}$，它整合了流行$\texttt{Bilby}$代码套件中NPE的能力。我们使用$\texttt{BilbyFlow}$分析了第三期LIGO-Virgo-KAGRA引力波瞬变目录（GWTC-3）中的38个大质量事件子集：29个事件（占比76%）的重要性采样效率＞1%，可在3分钟至1.5小时内生成可靠后验分布；其余事件的重要性采样效率远小于1%，运行时间最长可达35小时。我们实现的中位重要性采样效率为7%，与$\texttt{DINGO}$包大致相当。后续开发旨在大幅提升该效率，使运行时间更稳定地达到分钟级。$\texttt{BilbyFlow}$为开源软件，可通过pip安装。
英文摘要
Bayesian inference plays a central role in the new field of gravitational-wave astronomy. However, traditional Bayesian inference with stochastic samplers is computationally expensive, taking hours to days per event. Transformative changes are therefore required to enable the science of next-generation observatories whose event rates and signal-to-noise ratios will increase significantly over the current generation. Recent work has shown that neural posterior estimation (NPE) is a promising path forward. A neural net is trained to approximate the posterior distribution of gravitational-wave parameters, allowing generation of posterior samples in a fraction of the time required by stochastic samplers. In this work, we introduce $\texttt{BilbyFlow}$, which harnesses the power of NPE in the popular $\texttt{Bilby}$ code suite. We use $\texttt{BilbyFlow}$ to analyze a subset of 38 high-mass events from the third LIGO-Virgo-KAGRA Gravitational-Wave Transient Catalog (GWTC-3). For 29 events (76\%), we obtained an importance-sampling efficiency $>$1%, allowing us to produce reliable posterior distributions within 3 min - 1.5 hours. For the other events, with importance-sampling efficiency $\ll$1%, the run time can be as long as 35 hours. We achieve a median importance-sampling efficiency of 7%, which is roughly comparable to the $\texttt{DINGO}$ package. We aim to significantly improve this efficiency with further development to make the runtime more reliably $O(\text{min})$. $\texttt{BilbyFlow}$ is open source and $\texttt{pip}$-installable.
发表机构
- School of Physics and Astronomy, Monash University（莫纳什大学物理与天文学院）
- OzGrav: The ARC Centre of Excellence for Gravitational-Wave Discovery（OzGrav：ARC引力波发现卓越中心）
机构由 AI 辅助整理，请以论文原文为准。
