Stellar flare detection methods in TESS data: application and performance study

The detection of stellar flares is crucial for understanding the dynamic processes on the stellar surface and their potential impacts on the surrounding exoplanetary systems. A variety of methods are currently employed for flare detection, with machine learning approaches demonstrating significant potential in automating classification tasks, particularly in the analysis of astronomical time series data. This review provides an overview of the methods used for flare detection in TESS data, evaluating their performance and effectiveness. It explores traditional techniques alongside more advanced methods such as machine learning, highlighting their respective strengths and limitations. By addressing existing challenges and identifying promising approaches, this paper aims to inspire further research and development in this field.