Early identification of optical tidal disruption events

A science module for the Fink broker

¹ IRAP, Université de Toulouse, CNRS, CNES, UPS Toulouse, France
² Institute of Physics of the Czech Academy of Sciences Na Slovance 1999/2 182 00 Prague 8, Czech Republic
³ The Oskar Klein Centre, Department of Astronomy, Stockholm University AlbaNova SE-10691 Stockholm, Sweden
⁴ European Space Agency (ESA), European Space Astronomy Centre (ESAC) Camino Bajo del Castillo s/n 28692 Villanueva de la Cañada Madrid, Spain
⁵ Université Clermont Auvergne, CNRS, LPCA Clermont-Ferrand F-63000, France
⁶ Université Paris-Saclay, CNRS/IN2P3, IJCLab Orsay, France
⁷ Lomonosov Moscow State University, Sternberg Astronomical Institute Universitetsky 13 Moscow 119234, Russia
⁸ Centre for Astrophysics and Supercomputing, Swinburne University of Technology Mail Number H29 PO Box 218 31122 Hawthorn VIC, Australia
⁹ ARC Centre of Excellence for Gravitational Wave Discovery (OzGrav) John St Hawthorn VIC 3122, Australia

^★ Corresponding authors: This email address is being protected from spambots. You need JavaScript enabled to view it. ; This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 23 July 2025
Accepted: 20 November 2025

Abstract

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 RAINBOW 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.