一种基于中介线逼近法的神经网络引力弧拟合方法
A Neural Network Gravitational Arc Finder based on the Mediatrix filamentation Method
摘要：
自动电弧检测方法需要扫描正在进行的和下一代宽场成像调查，预计将包含数千个强大的透镜系统。电弧探测器也需要对电弧丰度的预测和观测进行定量比较。为此已经提出了几种算法，但机器学习方法仍然是弧查找过程中相对未开发的步骤。在这项工作中，我们介绍了一种新的电弧探测器的基础上模式识别，它使用一组来自Mediatrix丝状方法作为人工神经网络（ANN）的条目的形态测量。我们展示了一个完整的例子的应用程序的电弧探测器，第一次训练和验证人工神经网络的模拟弧，然后应用代码上的四个哈勃太空望远镜（HST）图像的强透镜系统。模拟弧使用简单的处方的镜头和源，同时模仿HST观测条件。我们还考虑HST图像中的对象样本，在ANN分类的训练中没有弧。我们使用训练和验证过程来确定一组合适的ANN配置，包括Mediatrix方法的输入组合，以最大限度地提高完整性，同时保持低误报率。在模拟中，该方法能够实现约90%的完整性，相对于预选后输入到ANN的弧。然而，在HST图像上，该完整性下降至$\sim$ 70%。错误检测是在这些图像中检测到的对象的3%的量级。Mediatrix测量与人工神经网络的组合是一个很有前途的工具，用于电弧查找的模式识别阶段。为了更好地训练和评估该方法的效率，需要更逼真的模拟和更大的真实系统集。
英文摘要：
Automated arc detection methods are needed to scan the ongoing and next-generation wide-field imaging surveys, which are expected to contain thousands of strong lensing systems. Arc finders are also required for a quantitative comparison between predictions and observations of arc abundance. Several algorithms have been proposed to this end, but machine learning methods have remained as a relatively unexplored step in the arc finding process. In this work we introduce a new arc finder based on pattern recognition, which uses a set of morphological measurements derived from the Mediatrix Filamentation Method as entries to an Artificial Neural Network (ANN). We show a full example of the application of the arc finder, first training and validating the ANN on simulated arcs and then applying the code on four Hubble Space Telescope (HST) images of strong lensing systems. The simulated arcs use simple prescriptions for the lens and the source, while mimicking HST observational conditions. We also consider a sample of objects from HST images with no arcs in the training of the ANN classification. We use the training and validation process to determine a suitable set of ANN configurations, including the combination of inputs from the Mediatrix method, so as to maximize the completeness while keeping the false positives low. In the simulations the method was able to achieve a completeness of about 90% with respect to the arcs that are input to the ANN after a preselection. However, this completeness drops to $\sim$ 70% on the HST images. The false detections are of the order of 3% of the objects detected in these images. The combination of Mediatrix measurements with an ANN is a promising tool for the pattern recognition phase of arc finding. More realistic simulations and a larger set of real systems are needed for a better training and assessment of the efficiency of the method.
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