发表机构
Carnegie Mellon University; Ludwig-Maximilians-Universität München（卡内基梅隆大学; 慕尼黑大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于归一化流的机器学习仿真器，用于高效估计LIGO/Virgo/KAGRA O4探测器网络下引力波事件的探测概率，并与蒙特卡洛重要性采样结果吻合，可提升总体分析的精度。
AI 中文摘要
在给定源参数的情况下，高效且准确地估计引力波事件的探测概率仍然是一个具有挑战性的问题，因为传统上这需要昂贵的蒙特卡洛注入活动。我们提出了一种基于机器学习的方法，使用归一化流（NFs）来估计LIGO/Virgo/KAGRA O4类探测器网络配置下引力波事件的探测概率。我们利用公开可用的注入数据集作为训练和测试数据。我们通过评估原始NF作为生成模型的性能来测试我们的方法和实现，该性能基于其恢复已探测事件正确参数分布的能力。此外，我们将NF仿真器预测的探测效率与蒙特卡洛重要性采样得到的探测效率进行比较，发现两者吻合良好。进一步地，对于包括具有尖锐特征在内的各种总体模型，估计探测效率可以比通过蒙特卡洛重要性采样获得的估计更精确。我们表明，类似于先前的神经网络方法，NF仿真器可用于引力波源的总体分析。本工作的训练代码和训练好的模型已公开提供。
英文摘要
Efficient and accurate estimation of the detection probability of a gravitational wave event given its source parameters remains a challenging problem because it conventionally requires expensive Monte Carlo injection campaigns. We present a machine learning-based approach using Normalizing Flows (NFs) to estimate the detection probability of gravitational wave events for a LIGO/Virgo/KAGRA O4-like detector network configuration. We utilize the publicly available injection dataset as training and test data. We test our method and implementation by evaluating the raw NF performance as a generative model based on its ability to recover the correct parameter distributions of detected events. In addition, we compare the detection efficiencies predicted by the NF emulator with those from Monte Carlo importance sampling and find good agreement. Furthermore, estimating the detection efficiency for a wide variety of population models, including those with sharp features, can be more precise than estimates obtained via Monte Carlo importance sampling. We show that the NF emulator can be used in population analysis of gravitational wave sources, similar to previous neural network approaches. The training code and trained models from this work are publicly available.
Comments21 pages, 10 figures, 2 tables; Code available at https://github.com/jgassert/graviflows
