Speaker
Description
Accurate and timely detection of solar flares is essential for advancing our understanding of solar activity and improving space weather forecasting capabilities. In this work, we evaluate the performance of an automated flare identification system by comparing its trigger outputs against two independent solar flare catalogs. The analysis highlights how detection performance depends critically on the characteristics of the reference dataset used for validation.
When evaluated against the operational NOAA flare catalog, which is based on fixed intensity thresholds and manual validation procedures, the system achieves a high flare level detection rate exceeding 96%. However, a large fraction of triggers remain unassociated with reported flares, suggesting a significant number of apparent false detections. This discrepancy reflects the inherent limitations of the operational catalog, which may exclude weaker, short duration, or low contrast events due to reporting thresholds and subjective validation.
To address this limitation, we repeat the analysis using a homogeneous archival catalog derived from systematic reprocessing of GOES soft X-ray observations. This catalog employs a consistent and automated flare detection methodology, including background correction and objective onset definitions. Under this framework, more than 93% of triggers are associated with flare intervals, and the fraction of unassociated triggers is significantly reduced. These results indicate that many detections classified as false positives under the operational catalog correspond to physically meaningful small scale activity captured by the archival dataset.
Detection performance is further analyzed as a function of flare magnitude and classification scheme. Detection rates increase with flare strength, approaching near-complete detection for the most energetic events. Differences between peak flux based and background corrected classifications highlight the role of background emission in flare identification and emphasize the importance of consistent event definitions.
Overall, this study demonstrates that the apparent performance of automated flare detection systems is strongly influenced by catalog completeness, threshold criteria, and timing definitions. The results underline the importance of using homogeneous and systematically processed datasets for validation and suggest that automated approaches, including those based on machine learning, can provide a more complete representation of solar activity by capturing events beyond traditional reporting limits.