用于引力波推断的归一化流嵌套采样
Nested Sampling with Normalising Flows for Gravitational-Wave Inference
摘要：
我们提出了一种新的方法，用于采样等似然轮廓嵌套采样使用一种类型的机器学习算法称为规范化流，并将其纳入我们的采样nessai。Nessai是专为计算的可能性是计算昂贵的问题，因此，训练一个规范化的流的成本是抵消的可能性评估的数量的整体减少。我们验证了我们的采样器上的128个模拟引力波信号从紧凑的二进制合并，并表明它产生无偏估计的系统参数。随后，我们将我们的结果与dynesty获得的结果进行比较，并发现计算的对数证据之间存在良好的一致性，同时需要少2.07倍的可能性评估。我们还强调了如何在nessai中并行化似然评估，而无需对算法进行任何修改。最后，我们概述了nessai中包含的诊断以及如何使用这些诊断来调整采样器的设置。
英文摘要：
We present a novel method for sampling iso-likelihood contours in nested sampling using a type of machine learning algorithm known as normalising flows and incorporate it into our sampler nessai. Nessai is designed for problems where computing the likelihood is computationally expensive and therefore the cost of training a normalising flow is offset by the overall reduction in the number of likelihood evaluations. We validate our sampler on 128 simulated gravitational wave signals from compact binary coalescence and show that it produces unbiased estimates of the system parameters. Subsequently, we compare our results to those obtained with dynesty and find good agreement between the computed log-evidences whilst requiring 2.07 times fewer likelihood evaluations. We also highlight how the likelihood evaluation can be parallelised in nessai without any modifications to the algorithm. Finally, we outline diagnostics included in nessai and how these can be used to tune the sampler's settings.
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。
