论文标题
在天文图像数据上的异常检测方法的比较
Comparison of outlier detection methods on astronomical image data
论文作者
论文摘要
在新一代天文仪器产生的巨大数据量所带来的许多挑战中，还需要寻找稀有和特殊的物体。无监督的离群值检测算法可能会提供可行的解决方案。在这项工作中，我们比较了六种方法的性能：局部离群因素，隔离森林，K-均值聚类，新颖性的度量以及正常和卷积自动编码器。将这些方法应用于从SDSS Stripe 82中提取的数据。在讨论了每种方法对其自己的超参数集的灵敏度之后，我们将每种方法的结果结合起来，以对对象进行排名并产生最终异常值列表。
Among the many challenges posed by the huge data volumes produced by the new generation of astronomical instruments there is also the search for rare and peculiar objects. Unsupervised outlier detection algorithms may provide a viable solution. In this work we compare the performances of six methods: the Local Outlier Factor, Isolation Forest, k-means clustering, a measure of novelty, and both a normal and a convolutional autoencoder. These methods were applied to data extracted from SDSS stripe 82. After discussing the sensitivity of each method to its own set of hyperparameters, we combine the results from each method to rank the objects and produce a final list of outliers.
