论文标题
通过Fink找到活跃的银河核
Finding active galactic nuclei through Fink
论文作者
论文摘要
我们介绍了当前在Fink代理中实现的主动银河核（AGN）分类器。特征是基于可用光度点的摘要统计数据，以及符号回归启用的颜色估计。学习阶段包括一个主动学习循环，用于从天文目录中报道的标签中构建优化的培训样本。使用此方法将Zwicky瞬态设施（ZTF）的真实警报分类，我们达到了98.0％的精度，93.8％的精度和88.5％的召回率。我们还描述了从即将到来的Vera C. Rubin天文台对空间和时间（LSST）进行处理数据所需的修改，并将其应用于扩展的LSST LSST天文学时间序列分类挑战的训练样本（ELASTICC）。结果表明，我们设计的功能空间可在此二进制分类任务中提供传统机器学习算法的高度性能。
We present the Active Galactic Nuclei (AGN) classifier as currently implemented within the Fink broker. Features were built upon summary statistics of available photometric points, as well as color estimation enabled by symbolic regression. The learning stage includes an active learning loop, used to build an optimized training sample from labels reported in astronomical catalogs. Using this method to classify real alerts from the Zwicky Transient Facility (ZTF), we achieved 98.0% accuracy, 93.8% precision and 88.5% recall. We also describe the modifications necessary to enable processing data from the upcoming Vera C. Rubin Observatory Large Survey of Space and Time (LSST), and apply them to the training sample of the Extended LSST Astronomical Time-series Classification Challenge (ELAsTiCC). Results show that our designed feature space enables high performances of traditional machine learning algorithms in this binary classification task.
