Superflare candidates in ~72000 G-stars

These networks are used to replace the manually visual inspection, which was a direct way of searching for superflares, and exclude false-positive events in recent years. Unlike other methods, which only used stellar light curves to search for superflare signals, we try to identify superflares through TESS pixel-level data with lower risk of mixing false-positive events and give more reliable identification results for statistical analysis.

The evaluated accuracy of each network is around 95.57%. After applying ensemble learning to these networks, the stacking method promotes accuracy to 97.62% with a 100% classification rate, and the voting method promotes accuracy to 99.42% with a relatively lower classification rate at 92.19%. We find that superflare candidates with short duration and low peak amplitude have lower identification precision, as their superflare features are hard to be identified.