Stars are not the steady, unchanging beacons they appear to be. Stellar flares—explosive magnetic events that can release energy equivalent to billions of nuclear bombs—constantly reshape our understanding of stellar physics and exoplanet habitability. But detecting these brief, brilliant outbursts in the ocean of photometric data from space missions like Kepler and TESS requires sophisticated analysis tools that can distinguish real flares from instrumental noise and systematic trends.
AltaiPony rises to this challenge with a comprehensive Python toolkit that not only identifies flare candidates in light curves but goes further by addressing a critical problem in flare astronomy: selection bias. The software performs injection-recovery analysis, artificially inserting synthetic flares into real data to measure how many get lost during de-trending and noise filtering. Built on the robust lightkurve foundation, it handles the full pipeline from data processing to statistical analysis, including power-law fitting for flare frequency distributions and energy-dependent recovery probability calculations.
This tool empowers researchers studying stellar magnetic activity, space weather around exoplanets, and the evolution of stellar rotation and magnetism. With peer-reviewed validation and active development, AltaiPony represents the kind of rigorous, reproducible science that modern astronomy demands—turning raw photons from space telescopes into quantitative insights about the violent, dynamic nature of stars throughout our galaxy.
⭐ Stars: 27
💻 Language: Jupyter Notebook
🔗 Repository: ekaterinailin/AltaiPony