Insight on Solar Flare Forecast With Explainable Deep Learning Grégoire Francisco [1,2], Dario Del Moro[1], Teresa Barata[2], João Fernandes[3], Sabrina Guastavino [4], Michele Piana[4] . [1] Department of Physics, University of Rome Tor Vergata , [2] IA, Instituto De Astroﬁsica E Ciências Do Espaço, University of Coimbra, [3] CITEUC, Geophysical and Astronomical Observatory, University of Coimbra [4] MIDA, Dipartimento di Matematica, Università degli Studi di Genova . Contact : gfrancisco@roma2.infn.it This research is part of the Space Weather Awareness Training Net- work (SWATNet) funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 955620 This research is also part of the Comprehensive Space Weather Studies for the ASPIS Prototype Realization (CAESAR) project, sup- ported by the Italian Space Agency and the National Institute of Astrophysics through the ASI-INAF n.2020- 35-HH.0 agreement for the development of the ASPIS prototype of scientiﬁc data centre for Space Weather. Acknowledgements • Coronal images showcase a 24h-window forecasting power close to the magnetogram • Each atmospheric layer consistently used by atmospheric models and contributing to improved performances • Overall Explainability Methods couple with Deep Learning proved to be useful to : - help to gain conﬁdence in models prediction and provide key information on the predictions quality - provide a purely data-driven approach to study and identify ﬂare precursors - identify model weakness and help to ﬁnd remedies • Polarity Sandwiches identiﬁed as potential new ﬂare precursors (they could also be an artefact of the pretrained models) • We are developing an automatic segmentation tool of Polarity Sandwiches based on Mathematical Morphology to perform a more systematic analysis of their correlation to the ﬂares • We are developing automatic detection tools of the other mentioned features to consolidate and generalize our statistical analysis • We are working on reproducing our approach on videos of the Corona to identify temporal/dynamic Coronal ﬂare precursors Conclusions and future works Solar Flares are sudden and violent release of magnetic energy from the Sun’s atmosphere in the form of electromagnetic radiation bursts. Being able to forecast Solar Flares accurately is essential to mitigate the risks associated with Space Weather, but it has been proven to be particularly challenging. Recently, Deep Learning Methods (DLMs) captured the interest of many re- searchers in the field and new data-driven approaches to the problem have been developed. With Explainability Methods (EMs) we can reverse this data-driven approach to gather insight on the actual physics of the events. We apply EMs to DLMs trained on Solar Corona images which perform as well as models trained on Solar Photosphere magnetograms alone to forecast Solar Flares in a 24h forecasting window. In these preliminary results we confirm that our models efficiently learn known physical precursors, such as the presence of sigmoidal coronal structures, which are observational signatures of highly twisted and sheared magnetic fields in the Solar Corona. From this proof of concept we also verified that DLMs trained on Solar Corona images can be efficient to forecast Flares near the Solar Limb, where it is harder for models using magnetograms alone. Model combining magnetograms and images of the different layers of the Solar Atmosphere seem to provide the best performances. Abstract Method We use Convolutional Neural Networks (CNN) to perform binary forecasts of C-class and higher ﬂares. We use the publicly available SDOBenchmark dataset consisting of 9000 AR crops covering Solar Cycle 24 peak (https://i4ds.github.io/SDOBenchmark/) . We split the training dataset in 10 folds by randomly separating AR numbers and perform 10 folds Cross-Validations (CV) to ﬁne-tune and evaluate our models. We use two distinct sets of fold for