暗能量巡天中的离群星系图像及其无监督机器学习识别
Outlier galaxy images in the Dark Energy Survey and their identification with unsupervised machine learning
AI总结：
针对暗能量巡天数据规模大、噪声导致假阳性多的离群天体识别难题，提出一种可控制假阳性率的基于机器学习的离群检测方法，能大幅缩减数据量，高效筛选稀有星系。
摘要：
暗能量巡天（Dark Energy Survey，DES）能够采集数量极其庞大的河外天体图像数据，我们有理由认为这些数据中潜藏着许多具有极高科学价值的特殊天体。由于DES数据的规模极大，在数百万个其他天体中识别出这类天体是一项极具挑战性的任务。噪声或饱和图像的存在进一步加剧了离群点检测的难度。当受检天体数量极高时，即使是很低的噪声率或假阳性率，也会导致数量极多的错误检测结果，使得自动化系统无法投入实际应用。本研究在暗能量巡天的首次数据发布中应用了一种自动化方法来检测离群天体。通过采用基于机器学习的离群点检测技术，该算法能够识别出在视觉上与数据库中绝大多数其他天体存在差异的天体。该算法的一项重要特性是支持对假阳性率进行控制，因此可用于实际的离群点检测工作。该算法在离群天体检测上并未达到完美的准确率，但它能够大幅缩减数据量，从而让离群点检测具备实际可行性。例如，将该算法应用于超过2×10^6张DES图像后，选取排名前250的天体即可得到一组稀有星系样本。若通过人工检查数据来构建这样的样本集，将耗费极其漫长的时间。
英文摘要：
The Dark Energy Survey is able to collect image data of an extremely large number of extragalactic objects, and it can be reasonably assumed that many unusual objects of high scientific interest are hidden inside these data. Due to the extreme size of DES data, identifying these objects among many millions of other celestial objects is a challenging task. The problem of outlier detection is further magnified by the presence of noisy or saturated images. When the number of tested objects is extremely high, even a small rate of noise or false positives leads to a very large number of false detections, making an automatic system impractical. This study applies an automatic method for automatic detection of outlier objects in the first data release of the Dark Energy Survey. By using machine learning-based outlier detection, the algorithm is able to identify objects that are visually different from the majority of the other objects in the database. An important feature of the algorithm is that it allows to control the false-positive rate, and therefore can be used for practical outlier detection. The algorithm does not provide perfect accuracy in the detection of outlier objects, but it reduces the data substantially to allow practical outlier detection. For instance, the selection of the top 250 objects after applying the algorithm to more than $2\cdot10^6$ DES images provides a collection of uncommon galaxies. Such collection would have been extremely time-consuming to compile by using manual inspection of the data.
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。
