Early identification of optical tidal disruption events

Context. 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. Automated and reliable classification pipelines that can select promising candidates in real time are required to identify TDEs in the vast alert streams produced by these surveys, however. Aims. We developed a module within the FINK alert broker to identify TDEs during their rising phase. The module was built to autonomously operate within the ZTF alert stream and to produce a list of candidates every night, enabling spectral and multiwavelength follow-up near peak brightness. Methods. All rising alerts were submitted to selection cuts and feature extraction using the R AINBOW multiband light-curve 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 for which the probability was high enough were inspected visually. Results. The classifier achieved 76% recall, which indicates a strong performance in early-phase identification, despite the limited available information before the peak. Out of the known TDEs that passed the selection cuts, half were flagged as TDEs before they had risen half the way. This proves that an early classification is possible. Additionally, new candidates were identified by applying the classifier on archival data, including a likely repeated TDE and some potential TDEs that occurred in active galaxies. The module is implemented in the FINK alert-processing framework and each night reports a small number of candidates to dedicated communication channels through a user-friendly interface for manual vetting and potential follow-up.