标题： 利用循环神经网络和卷积神经网络分析强引力透镜的干涉观测结果 显示英文标题

标题： Analyzing interferometric observations of strong gravitational lenses with recurrent and convolutional neural networks

摘要： 我们使用卷积神经网络（CNNs）和循环神经网络（RNNs）来估计强引力透镜的参数，这些参数是从干涉测量观测中获取的。我们探索了多种策略，并发现当从图像中去除脏束效应时，效果最佳，这通过基于RNN的结构执行去卷积后进行参数估计。为此，我们使用了Putzky & Welling（2017）中引入的循环推理机（RIM）。这为传统的CLEAN算法提供了一个快速且自动化的替代方案。我们利用伯努利分布的变分推理方法获得估计参数的不确定性。我们用模拟测试数据集以及五个ALMA强透镜观测数据来测试网络性能。对于观测到的ALMA数据，我们将我们的估计值与最大似然透镜建模方法得到的值进行比较，该方法在可见度空间中运行，并发现结果一致。我们表明，通过结合执行图像去卷积的RNN结构和执行透镜分析的CNN结构，我们可以高精度地估计透镜参数，其不确定性比最大似然方法高出不到两倍。包括由RIM执行的去卷积程序，在单个GPU上一次评估大约需要一秒钟，相比uv平面上的最大似然透镜建模，分析速度提高了六个数量级，而使用的计算资源减少了八个数量级。我们得出结论，这是一种很有前景的方法，可用于当前设施（如ALMA、JVLA）和未来大型干涉测量天文台（如SKA）的毫米波和厘米波干涉数据的分析，因为在uv平面上进行分析可能困难或不可行。 显示英文摘要

摘要： We use convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to estimate the parameters of strong gravitational lenses from interferometric observations. We explore multiple strategies and find that the best results are obtained when the effects of the dirty beam are first removed from the images with a deconvolution performed with an RNN-based structure before estimating the parameters. For this purpose, we use the recurrent inference machine (RIM) introduced in Putzky & Welling (2017). This provides a fast and automated alternative to the traditional CLEAN algorithm. We obtain the uncertainties of the estimated parameters using variational inference with Bernoulli distributions. We test the performance of the networks with a simulated test dataset as well as with five ALMA observations of strong lenses. For the observed ALMA data we compare our estimates with values obtained from a maximum-likelihood lens modeling method which operates in the visibility space and find consistent results. We show that we can estimate the lensing parameters with high accuracy using a combination of an RNN structure performing image deconvolution and a CNN performing lensing analysis, with uncertainties less than a factor of two higher than those achieved with maximum-likelihood methods. Including the deconvolution procedure performed by RIM, a single evaluation can be done in about a second on a single GPU, providing a more than six orders of magnitude increase in analysis speed while using about eight orders of magnitude less computational resources compared to maximum-likelihood lens modeling in the uv-plane. We conclude that this is a promising method for the analysis of mm and cm interferometric data from current facilities (e.g., ALMA, JVLA) and future large interferometric observatories (e.g., SKA), where an analysis in the uv-plane could be difficult or unfeasible.