警报异常检测器（AHA）：在ZTF暂现警报流中进行异常检测
Anomaly Hunter for Alerts (AHA): Anomaly Detection in the ZTF Transient Alert Stream
AI总结：
AHA通过自编码器检测ZTF警报流中的异常暂现体和超新星，识别87个候选对象，具有高效的数据利用和高召回率。
AI中文摘要：
现代时间域调查产生的警报流规模庞大，使得彻底的手动检查不可行，需要自动化方法来识别异常的暂现体以供后续跟进。在本工作中，我们介绍了一个无监督的异常检测流程，应用于ZTF警报流，使用Lasair经纪人。我们将正常对象定义为Ia型超新星、II型超新星和Ib/c型超新星。异常对象包括（i）更奇特的暂现体（AGN、TDEs、SLSNe、CVs和核暂现体）以及（ii）被标记为超新星的对象，无论是通过光谱学还是通过Lasair，具有异常属性，如错误或缺失的宿主关联，或者非超新星样光变曲线。我们的流程由三个独立训练的简单自编码器组成，分别处理不同的警报流数据产品：对象特征、三元组图像截取和光变曲线。每个模型主要在正常暂现体上进行训练，性能通过在光谱分类的保留测试集和实时警报流中异常对象的召回率以及所有异常对象的纯度来评估。在测试集中，性能在固定排名（对应前十个评分候选者）上进行评估，而在警报流中则使用从测试集行为中定义的异常阈值进行评估。在两种情况下，算法都能在它们的前几名候选者中恢复异常暂现体和异常超新星。在25天的实时警报流应用中，我们识别出87个异常超新星候选者以供后续跟进。在测试集中，不同自编码器标记的异常的重叠不存在，而在警报流中重叠很小，任何两个算法之间的最大重叠为11个对象。该框架数据高效，仅需几千个训练示例，使其非常适合用于Rubin Observatory警报流的早期和正在进行的应用。
英文摘要：
Modern time-domain surveys produce alert streams at a scale that makes exhaustive manual inspection infeasible, requiring automated methods to identify unusual transients for follow-up. In this work, we present an unsupervised anomaly detection pipeline applied to the ZTF alert stream using the Lasair broker. We define normal objects as SN Ia, SN II, and SN Ib/c. Anomalous objects include (i) more exotic transients (AGN, TDEs, SLSNe, CVs, and nuclear transients) and (ii) supernova-labeled objects, either spectroscopically or by Lasair, with anomalous properties, such as incorrect or absent host associations, or non-supernova-like light curves. Our pipeline consists of three independently trained simple autoencoders operating on distinct alert stream data products: object features, triplet image cutouts, and light curves. Each model is trained on predominantly normal transients, and performance is assessed using the recall of exotic objects and the purity of all anomalous objects across both a spectroscopically classified held-out test set and the live alert stream. In the test set, performance is evaluated at a fixed rank corresponding to the top ten scoring candidates, while in the alert stream it is evaluated using an anomaly threshold defined from test set behavior. Across both settings, the algorithms consistently recover exotic transients and anomalous supernovae among their top-ranked candidates. Over 25 days of live alert stream application, we identify 87 unusual supernova candidates for follow-up. The overlap between anomalies flagged by different autoencoders in the test set is non-existent, and in the alert stream is small, with maximum overlap between any two algorithms being 11 objects. The framework is data-efficient, requiring only a few thousand training examples, making it well suited for early and ongoing application to the Rubin Observatory alert stream.
