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Gaia method paper: an algorithm to detect flare events in Gaia time-series

Publication date2026
Abstract

The third Gaia data release (GDR3) includes the gdr3_rotmod catalogue of 474026 stars with variability that is attributed to magnetic activity analogous to that observed in the Sun, arising from rotational modulation induced by spots and faculae. These stars are therefore expected to exhibit flaring activity driven by magnetic reconnection processes. We aim to demonstrate that stellar flares can be reliably identified and characterised in the sparsely sampled \gaia\ photometric time series. We developed and validated a dedicated flare-detection pipeline that exploits the simultaneous multi-band photometry in the $G$, $G_{\rm BP}$, and $G_{\rm RP}$ bands. The algorithm identifies outliers associated with a concurrent increase in brightness and blueing of the stellar colour, and applies consistency criteria to distinguish genuine flare events from instrumental or calibration artefacts. Applying this method to the gdr3_rotmod catalogue, we detect 3217 flares occurring on 2818 stars out of 474026 analysed sources. All events are provided in a dedicated catalogue together with their photometric amplitudes. For a subset of 348 flares, we estimate an effective temperature using a black-body approximation, and we identify 29 hyper-flares with amplitudes $A(G)\ge0.75$ mag, which occur preferentially in M dwarfs. Despite the sparse temporal sampling of Gaia, our results demonstrate that its multi-band photometry and spectrophotometric information enable the robust detection of flares and the basic characterisation of their properties across an all-sky stellar sample. This work establishes the methodological foundation for future Gaia flare catalogues and provides a homogeneous, complementary view of stellar flaring activity alongside high-cadence missions such as Kepler and TESS.

Keywords
  • Solar and Stellar Astrophysics (astro-ph.SR)
  • FOS: Physical sciences
Citation (ISO format)
DISTEFANO, E. et al. Gaia method paper: an algorithm to detect flare events in Gaia time-series. 2026. doi: 10.48550/arxiv.2609.20417
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Creation30/09/2026 00:31:23
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