Legnaro, E., et al, 2025, Deep Learning for Active Region Classification: A Systematic Study from Convolutional Neural Networks to Vision Transformers, Astrophysical Journal, 981, 157, doi:10.3847/1538-4357/adb41a.
Rossi, M., et al, 2025, Extended drag-based model for better predicting the evolution of coronal mass ejections, Astronomy & Astrophysics, 694, A247, doi:10.1051/0004-6361/202452288.
Walsh, J., et al, 2024, A Foundation Model for the Solar Dynamics Observatory, arXiv:2410.02530.
Guastavino, S., et al, 2024, Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches, Astrophysical Journal, 971, 94, doi:10.3847/1538-4357/ad5b57.
Muñoz-Jaramillo, A., et al, 2024, Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms, Astrophysical Journal Supplement Series, doi:10.3847/1538-4365/ad12c2.
Georgoulis, M.K., et al, 2024, Prediction of Solar Energetic Events Impacting Space Weather Conditions, Advances in Space Research, doi: 10.1016/j.asr.2024.02.030.
Perracchione, E., et al, 2023, Unbiased CLEAN for STIX in Solar Orbiter, Astrophysical Journal Supplement Series, 268, 2, doi:10.3847/1538-4365/acf669.
Guastavino, S., et al, 2023, Physics-driven Machine Learning for the Prediction of Coronal Mass Ejections’ Travel Times, Astrophysical Journal, 954 151, doi: 10.3847/1538-4357/ace62d.
Burrell, A. G., Coxon, J., Aye, K.-M., Lamarche, L., Murray, S. A., eds., 2023, Snakes on a Spaceship—An Overview of Python in Space Physics, Lausanne: Frontiers Media SA, doi: 10.3389/978-2-8325-2959-1.
