SNAD catalogue of M-dwarf flares from the Zwicky Transient Facility

Monthly Notices of the Royal Astronomical Society. 2024. Vol. 533. No. 4. P. 4309–4323.

Voloshina A., Lavrukhina A., Pruzhinskaya M., Malanchev K., Ishida E. O., Krushinsky V. V., Aleo P., Gangler E., Kornilov M., Korolev V., Russeil E., Semenikhin T. A., Sreejith S., Volnova A.

Most of the stars in the Universe are M spectral class dwarfs, which are known to be the source of bright and frequent stellar flares. In this paper, we propose new approaches to discover M-dwarf flares in ground-based photometric surveys. We employ two approaches: a modification of a traditional method of parametric fit search and a machine learning algorithm based on active anomaly detection. The algorithms are applied to Zwicky Transient Facility (ZTF) data release 8, which includes the data from the ZTF high-cadence survey, allowing us to reveal flares lasting from minutes to hours. We analyse over 35 million ZTF light curves and visually scrutinize 1168 candidates suggested by the algorithms to filter out artefacts, occultations of a star by an asteroid, and other types of known variable objects. The result of this analysis is the largest catalogue of ZTF flaring stars to date, representing 134 flares with amplitudes ranging from −0.2 to −4.6 mag, including repeated flares. Using Pan-STARRS DR2 colours, we assign a spectral subclass to each object in the sample. For 13 flares with well-sampled light curves and available geometric distances from Gaia DR3, we estimate the bolometric energy. This research shows that the proposed methods combined with the ZTF’s cadence strategy are suitable for identifying M-dwarf flares and other fast transients, allowing for the extraction of significant astrophysical information from their light curves.

Keywords: machine learning