A Machine Learning Approach for Identifying Coronal Mass Ejection–associated Stellar Flares in TESS Observations

Abstract Coronal mass ejections (CMEs) are major drivers of stellar space weather and can strongly influence the habitability of exoplanets. However, compared to the frequent occurrence of white-light flares, confirmed stellar CMEs remain extremely rare. Whether such flares are commonly accompanied by CMEs is a key question for solar-stellar comparative studies. Using Sun-as-a-star soft X-ray flare light curves observed by the Geostationary Operational Environmental Satellites (GOES) X-ray Sensor (XRS) 1–8 Å channel, we compiled a sample of 1766 M-class and larger solar flares and extracted features with both deep convolutional neural networks and manual methods. Five machine learning classifiers were trained to distinguish eruptive from confined flares, with the random forest (RF) model achieving the best performance (true skill statistic; TSS = 0.31). This TSS value indicates that the model possesses a moderate ability to discriminate between eruptive and confined flares. Normalized white-light and GOES XRS flare light curves show broadly consistent temporal evolution, reflecting their shared energy-release history and supporting a probabilistic transfer of the model to white-light flare data. We applied the best-performing RF model to 41,405 Transiting Exoplanet Survey Satellite–detected flares on FGKM-type main-sequence stars, predicting that approximately 47% of events show CME-like morphological characteristics, with the model-implied intrinsic association fraction lying in the range 35%–60%. Intriguingly, the CME occurrence rate decreases with increasing flare energy, indicating that the most energetic flares may be more strongly confined by overlying magnetic fields. These results provide new insight into flare–CME connections in diverse stellar environments and have important implications for assessing the impact of stellar eruptive activity on exoplanetary atmospheres.