光学潮汐破坏事件的早期识别：Fink broker中的一个科学模块
Early Identification of Optical Tidal Disruption Events: A science module for the Fink broker
AI总结：
该研究开发了一个模块，用于在Fink警报处理框架中实现TDEs的早期识别，通过XGBoost分类器和多波段光变曲线拟合，提高了TDEs的检测效率。
AI中文摘要：
潮汐破坏事件（TDEs）的检测是诸如Zwicky瞬态设施（ZTF）和即将进行的Vera C. Rubin天文观测遗产调查（LSST）等大规模光学时间域调查的关键科学目标之一。然而，这些调查产生的大量警报流中识别TDEs需要自动化且可靠的分类管道，能够实时选择有前途的候选者。我们开发了一个模块，用于在TDEs上升阶段内识别TDEs。该模块旨在在ZTF警报流中自主运行，每天生成候选者列表，并在亮度达到峰值时进行光谱和多波段后续观测。所有上升警报都通过Rainbow多波段光变曲线拟合进行选择切片和特征提取。最佳拟合值被用作训练XGBoost分类器的输入，目标是识别TDEs。训练集是使用ZTF观测数据构建的，这些数据对象在瞬态名称服务器中有可用的分类。最后，概率较高的候选者被人工检查。分类器的召回率为76%，表明在峰值前信息有限的情况下，其在早期阶段识别方面表现良好。我们证明，已知的通过选择切片的TDEs中，有一半在上升阶段中途被标记为TDEs，证明了早期分类的可行性。此外，通过将分类器应用于存档数据，识别出新的候选者，包括一个可能的重复TDE和一些发生在活动星系中的潜在TDEs。该模块已整合到Fink警报处理框架中，每天通过用户友好的界面向专门的通信通道报告少量候选者，供人工审查和潜在的后续观测。
英文摘要：
The detection of tidal disruption events (TDEs) is one of the key science goals of large optical time-domain surveys such as the Zwicky Transient Facility (ZTF) and the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time. However, identifying TDEs in the vast alert streams produced by these surveys requires automated and reliable classification pipelines that can select promising candidates in real time. We developed a module within the Fink alert broker to identify TDEs during their rising phase. It was built to autonomously operate within the ZTF alert stream, producing a list of candidates every night and enabling spectral and multi-wavelength follow-up near peak brightness. All rising alerts are submitted to selection cuts and feature extraction using the Rainbow multi-band lightcurve fit. Best-fit values were used as input to train an XGBoost classifier with the goal of identifying TDEs. The training set was constructed using ZTF observations for objects with available classification in the Transient Name Server. Finally, candidates with high enough probability were visually inspected. The classifier achieves 76% recall, indicating strong performance in early-phase identification, despite the limited available information before peak. We show that, out of the known TDEs that pass the selection cuts, half of them are flagged as TDE before halfway in their rise, proving the feasibility of early classification. Additionally, new candidates were identified by applying the classifier on archival data, including a likely repeated TDE and some potential TDEs occurring in active galaxies. The module is implemented into the Fink alert processing framework, reporting each night a small number of candidates to dedicated communication channels through a user-friendly interface, for manual vetting and potential follow-up.
