The Sun is an enigmatic star that produces the most powerful explosive events in our solar system – solar flares and coronal mass ejections. Studying these phenomena can provide a unique opportunity to develop a deeper understanding of fundamental processes on the Sun, and critically, to better forecast space weather. The Active Region Classification and Flare Forecasting (ARCAFF) project will develop a beyond state-of-the-art flare forecasting system utilising end-to-end deep learning (DL) models to significantly improve upon traditional flare forecasting capabilities.

The large amount of available space-based solar observations are an ideal candidate for this type of analysis, given DL effectiveness in modelling complex relationships. DL has already been successfully developed and deployed in weather forecasting, financial services, and health care domains, but has not been fully exploited in the solar physics domain.

ARCAFF will increase the accuracy and timeliness of current operational flare forecast products and create new time series flare forecasts with uncertainties. The forecasts will be benchmarked against current systems using international community standards. The datasets and codes developed for ARCAFF will be made openly available to support further research efforts and encourage their re-use.

The five key objectives of ARCAFF are:

1. Active region classifications using magnetogram cutouts
2. Active region localisation and classification using full disk magnetograms
3. Point-in-time flare prediction using full disk magnetograms
4. Point-in-time flare prediction using full disk multimodal observations
5. Time series flare prediction based on time series of full disk multimodal observation