AI 中文总结
研究利用自动编码器对隐匿活动星系核光变曲线进行特征驱动的异常标记，通过无监督算法在提取的特征上训练，用SHAP方法表征，标记出11.18%的异常AGN，确定细化特征子集，深入分析异常，为理解AGN异常提供见解。
AI 中文摘要
活动星系核（AGN）是最复杂的天体物理对象类别之一，具有广泛的变异性和观测特性。识别异常AGN对于更好地理解其发射背后的物理机制以及发现潜在的新子类或罕见行为至关重要。随着来自下一代调查的数据量增加，基于机器学习的异常检测为系统地标记和研究此类异常值提供了一种有前途的方法。我们探索使用具有特征驱动方法的无监督算法来标记异常AGN，并由人类专家进一步探索。主要关注隐匿AGN，其往往更难表征。我们使用的算法是自动编码器，它是在从光变曲线提取的特征上进行训练，而不是直接处理光变曲线。该方法的无监督性质允许在不依赖标记数据的情况下检测异常。为了正确表征特征空间和检测过程，我们使用了SHAP方法。我们的方法将所研究的AGN中的11.18%标记为异常。我们特别关注异常隐匿AGN，并确定了一个细化的特征子集，其性能与完整集相当。连同对异常的深入分析，这为自动编码器如何分配异常状态以及哪些特征最能指示天体物理上有趣的行为或现象提供了见解。
英文摘要
Active galactic nuclei (AGN) are among the most complex classes of astrophysical objects, displaying a wide range of variability and observational properties. Identifying unusual AGN is crucial for understanding the physical mechanisms behind their emission better and for discovering potentially new subclasses or rare behaviors. With the increasing volume of data from next-generation surveys, machine-learning-based anomaly detection offers a promising approach to flagging and investigating such outliers systematically. We explore the use of unsupervised algorithms with a feature-driven approach to flag anomalous AGN, further explored by a human expert. The main focus is on obscured AGN, which tend to be harder to characterize. The algorithm we used was an AutoEncoder, which we trained on features extracted from the light curves rather than working with the light curves directly. The unsupervised nature of the method allows the detection of anomalies without relying on labeled data. To properly characterize the feature space and the detection process, we used the SHAP method. Our method flagged $11.18\%$ of the AGN we studied as anomalous. We focused in particular on anomalous obscured AGN and identified a refined subset of features that yields a comparable performance to the full set. Together with an in-depth analysis of the anomalies, this provides insight into how the AutoEncoder assigns anomalous status and which features are most indicative of astrophysically interesting behaviors or phenomena.
