论文标题
无监督的机器学习，以更深，更宽，更快的光曲线中的瞬时发现
Unsupervised machine learning for transient discovery in Deeper, Wider, Faster light curves
论文作者
论文摘要
在时间域调查中识别异常光曲线通常具有挑战性。此外，随着广场调查的数量越来越多，产生的数据量超过了天文学家进行手动评估的能力，因此异常和异常检测对于瞬时科学变得至关重要。我们提出了一种使用聚类技术和天文包装的无监督方法，用于瞬时发现。作为概念验证，我们使用两种不同的望远镜抖动策略评估了在两个1.5小时内收集的85553分钟的光曲线，作为更深层更广泛，更快的程序的一部分。通过将HDBSCAN聚类技术与隔离森林异常检测算法相结合，我们能够快速分离异常源以进行进一步分析。我们成功地从田地内的一系列目录中成功恢复了已知的变量源，并找到了另外7个未经战机变量和两个恒星耀斑事件，包括从可能的M-Dwarf中很少观察到的超快速耀斑（5分钟）。
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.
