Lost and found: A gallery of overlooked optical nuclear transients from the ZTF archive

Le résumé fourni par la source

Context. Tidal disruption events (TDEs) correspond to a star’s destruction by the tidal forces around a black hole, leading to outbursts, which can last from months to years. These transients are rare, and increasing the current sample is paramount for understanding them. As part of the F INK alert broker, we developed an early detection system for TDEs in Zwicky Transient Facility (ZTF) data. Aims. In this paper, we report the optical transients found either during this tool’s development, or through application of the classifier to the existing archive. We used this sample to anticipate the improvements that will need to be implemented in TDE detection systems for future surveys. Methods. For all the transients, we present optical and infrared archival photometry from ZTF, WISE, and Catalina, along with an assessment of each host’s previous nuclear activity. We fitted the ZTF light curves with both a phenomenological and a physically motivated model. Results. We report a total of 19 optical nuclear transients: nine in passive galaxies, eight in active galactic nuclei (AGNs), and two with uncertain host activity. We identify two newly discovered repeating TDE candidates and compare them to the current sample of repeating optical nuclear transients. One transient is exceptionally long-lived (> 5 years) in a previously passive galaxy. Three flares in active galaxies have absolute g -band magnitudes brighter than −24 mag, making them new extreme nuclear transient candidates. One seemingly repeating object was revealed to be two independent supernovae in the same galaxy. In the absence of spectral follow-up, definitive classification of most transients remains difficult, and some may be powered by AGN variability. Conclusions. This sample shows both the potential of our detection system for future discoveries and the value of archival searches in revealing overlooked transients. It also raises several points of concern and avenues of improvement for current and future classifiers.

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