无监督机器学习用于更深、更宽、更快光变曲线中的瞬态发现
Unsupervised machine learning for transient discovery in Deeper, Wider, Faster light curves
摘要：
在时域测量中识别异常光变曲线通常具有挑战性。此外，随着越来越多的广域观测和产生的数据量超过天文学家的人工评估能力，异常值和异常检测对瞬态科学变得至关重要。我们提出了一个无监督的方法，使用聚类技术和Astronomaly包的瞬态发现。作为概念的证明，我们评估85553分钟节奏的光变曲线收集了两个1.5小时的时间，作为更深，更宽，更快的程序的一部分，使用两种不同的望远镜抖动策略。通过将聚类技术HDBSCAN与隔离森林异常检测算法通过Astronomaly的可视化界面相结合，我们能够快速隔离异常源以进行进一步分析。我们成功地恢复了已知的变量源，在一系列的目录从字段内，并找到了另外7个未编目的变量和两个恒星耀斑事件，包括一个罕见的观察到的超快耀斑（5分钟）从一个可能的M-矮星。
英文摘要：
Identification of anomalous light curves within time-domain surveys is often challenging. In addition, with the growing number of wide-field surveys and the volume of data produced exceeding astronomers ability for manual evaluation, outlier and anomaly detection is becoming vital for transient science. We present an unsupervised method for transient discovery using a clustering technique and the Astronomaly package. As proof of concept, we evaluate 85553 minute-cadenced light curves collected over two 1.5 hour periods as part of the Deeper, Wider, Faster program, using two different telescope dithering strategies. By combining the clustering technique HDBSCAN with the isolation forest anomaly detection algorithm via the visual interface of Astronomaly, we are able to rapidly isolate anomalous sources for further analysis. We successfully recover the known variable sources, across a range of catalogues from within the fields, and find a further 7 uncatalogued variables and two stellar flare events, including a rarely observed ultra fast flare (5 minute) from a likely M-dwarf.
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