Speaker
Description
Solar flares represent a major focus in space weather research due to their potential to disrupt satellite operations and critical terrestrial technologies. These phenomena are characterized by rapid variations in X-ray flux, resulting in intense energy releases within the solar atmosphere. To better understand their behavior, this study has utilized the sunpy library to access the X-ray flux time series from the Geostationary Operational Environmental Satellite (GOES) for eleven pre-selected events and to analyze the variations of soft X-ray flux and the temperature and emission measures of each event, aiming to identify precursors of solar flares. Furthermore, an image database is being constructed using four extreme ultraviolet channels (94 Å, 131 Å, 171 Å, and 193 Å) from the Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory (SDO). The objective is to use these data to train machine learning models for the automated recognition and phase characterization of solar flares. Ultimately, the integration of GOES and SDO data aims to contribute to the identification of future events, thereby aiding in the monitoring and forecasting of space weather.