寻找强引力透镜的挑战
The Strong Gravitational Lens Finding Challenge
摘要：
大规模的成像调查将增加星系尺度强透镜候选者的数量，可能比今天已知的数量多三个数量级。要找到这些罕见的天体，需要从至少数千万张图像中挑选出来，并从中得出科学结果，这需要量化任何搜索方法的效率和偏差。为了实现这些目标，必须开发自动化方法。由于引力透镜是罕见的物体，减少误报将特别重要。我们介绍了开放引力透镜寻找挑战的描述和结果。参与者被要求对10万个候选物体进行分类，以确定它们是否是引力透镜，目的是开发更好的自动化方法，以便在大型数据集中找到透镜。使用了多种方法，包括目视检查、电弧和环形探测器、支持向量机（SVM）和卷积神经网络（CNN）。我们发现，许多方法将很容易足够快，以分析预期的数据流。在测试数据中，有几种方法能够在对透镜特征（如透镜图像亮度、大小或与透镜星系的对比度）应用一些阈值后识别超过一半的透镜，而不会进行单一的假阳性识别。这比人类直接检查要好得多。（节略）
英文摘要：
Large scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100,000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. (abridged)
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。
