Flare Set-Prediction Transformer: A Transformer-Based Set-Prediction Model for Detailed Solar Flare Forecasting

Solar flare prediction models typically use classification, predicting only the probability of categorized events within a time window. This misses critical information, such as how many flares occur, their precise timings, and their intensities. To address this, we propose a paradigm shift to set prediction, directly forecasting a variable-sized set of flare events with detailed characteristics. We demonstrate this approach with FSPT (Flare Set-Prediction Transformer), a transformer-based model adapted from object detection principles. FSPT predicts sets containing individual flare start, peak, and end time offsets, as well as peak X-ray intensity. This work presents the set-prediction framework and the FSPT model, showing its potential for more informative flare forecasting.