A Machine-Learning Approach for Identifying CME-Associated Stellar Flares in TESS Observations

TL;DR

This study employs machine learning to analyze TESS stellar flare data, estimating that nearly half of flares may be CME-associated, revealing a decrease in CME likelihood with increasing flare energy, and advancing understanding of stellar space weather.

Contribution

The paper introduces a machine-learning framework trained on solar flare data to predict CME association in stellar flares observed by TESS, a novel approach in stellar space weather research.

Findings

Approximately 47% of TESS flares show CME-like features.

CME occurrence rate decreases with increasing flare energy.

The random forest classifier achieved a TSS of 0.31 in distinguishing eruptive from confined flares.

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 GOES XRS 1--8~\AA\ channel, we compiled a sample of 1,766 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 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 TESS-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.