Francesco Pio Ramunno

Francesco Pio Ramunno

PhD researcher in Deep Learning applied to Solar Physics

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Enhancing Image Resolution of Solar Magnetograms: A Latent Diffusion Model Approach

Published in Astronomy and Astrophysics, 2025

The spatial properties of the solar magnetic field are crucial to decoding the physical processes in the solar interior and their inter planetary effects. However, observations from older instruments, such as the Michelson Doppler Imager (MDI), have limited spatial or temporal resolution, which hinders the ability to study small-scale solar features in detail. Super resolving these older datasets is essential for uniform analysis across different solar cycles, enabling better characterization of solar flares, active regions, and magnetic network dynamics. In this work, we introduce a novel diffusion model approach for Super-Resolution and we apply it to MDI magne tograms to match the higher-resolution capabilities of the Helioseismic and Magnetic Imager (HMI). By training a Latent Diffusion Model (LDM)withresiduals on downscaled HMI data and fine-tuning it with paired MDI/HMI data, we can enhance the resolution of MDIobservations from 2”/pixel to 0.5”/pixel. We evaluate the quality of the reconstructed images by means of classical metrics (e.g., PSNR, SSIM, FID and LPIPS) and we check if physical properties, such as the unsigned magnetic flux or the size of an active region, are preserved. We compare our model with different variations of LDM and Denoising Diffusion Probabilistic models (DDPMs), but also with two deterministic architectures already used in the past for performing the Super-Resolution task. Furthermore, we show with an analysis in the Fourier domain that the LDM with residuals can resolve features smaller than 2”, and due to the probabilistic nature of the LDM, we can asses their reliability, in contrast with the deterministic models. Future studies aim to super-resolve the temporal scale of the solar MDI instrument so that we can also have a better overview of the dynamics of the old events.

Recommended citation: Ramunno, F. P., Massa, P., Kinakh, V., Panos, B., Csillaghy, A., & Voloshynovskiy, S. (2025). Enhancing Image Resolution of Solar Magnetograms: A Latent Diffusion Model Approach. arXiv preprint. arXiv:2503.24271.
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Magnetogram-to-Magnetogram: Generative Forecasting of Solar Evolution

Published in Proceedings of SPAICE2024: The First Joint European Space Agency / IAA Conference on AI in and for Space, 17-19 September 2024, 2024

Investigating the solar magnetic field is crucial to understand the physical processes in the solar interior as well as their effects on the interplanetary environment. We introduce a novel method to predict the evolution of the solar line-of-sight (LoS) magnetogram using image-to-image translation with Denoising Diffusion Probabilistic Models (DDPMs). Our approach combines “computer science metrics” for image quality and “physics metrics” for physical accuracy to evaluate model performance. The results indicate that DDPMs are effective in maintaining the structural integrity, the dynamic range of solar magnetic fields, the magnetic flux and other physical features such as the size of the active regions, surpassing traditional persistence models, also in flaring situation. We aim to use deep learning not only for visualisation but as an integrative and interactive tool for telescopes, enhancing our understanding of unexpected physical events like solar flares. Future studies will aim to integrate more diverse solar data to refine the accuracy and applicability of our generative model. Visit https://github.com/fpramunno/MAG2MAG for the code and https://huggingface.co/spaces/fpramunno/mag2mag for the interactive tool.

Recommended citation: Ramunno, F. P., Jeong, H.-J., Hackstein, S., Csillaghy, A., Voloshynovskiy, S., & Georgoulis, M. K. (2024). "Magnetogram-to-Magnetogram: Generative Forecasting of Solar Evolution." Proceedings of SPAICE2024: The First Joint European Space Agency / IAA Conference on AI in and for Space, 75-80. doi: 10.5281/zenodo.13885515.
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Solar synthetic imaging: Introducing denoising diffusion probabilistic models on SDO/AIA data

Published in Astronomy & Astrophysics, 2024

For the luck of humanity, there are way less big solar flares than small ones. Even if these are good news, this makes it challenging to train machine learning algorithms able to model solar activity. As a result, solar monitoring applications, including flare forecasting, suffer from this lack of input data. To overcome this issue, generative deep learning models can be utilised to produce synthetic images representing solar activity and thus compensating the rarity of big events. This study aims to develop a method that can generate synthetic images of the Sun with the ability to include flare of a specific intensity. To achieve our goals, we introduce a Denoising Diffusion Probabilistic Model (DDPM). We train it with a carefully crafted dataset from the Atmospheric Image Assembly (AIA) instrument on the SDO spacecraft, specifically the 171 Å band, which captures images of coronal loops, filaments, flares, and active regions. GOES X-ray measurements are employed to classify each image based on the solar flare scale (A, B, C, M, X), after selecting the flaring images from AIA using the Heliophysics Event Knowledgebase, which allows for temporal localisation of the flaring events. The generative model performance is evaluated using cluster metrics, Fréchet Inception Distance (FID), and the F1-score. We demonstrate state-of-the-art results in generating solar images and conduct two experiments that use the synthetic images. The first experiment trains a supervised classifier to identify those events. The second experiment trains a basic solar flare predictor. The experiments demonstrate the effectiveness of additional synthetic samples to addressing the problem of imbalanced datasets. We believe this is only the beginning of DDPM use with solar data. It remains to gain a better understanding of the generation capabilities of the denoising diffusion probabilistic models in the contest of solar flare predictions and apply them to other deep learning and physical tasks, such as AIA to HMI (Helioseismic and Magnetic Imager) image translation.

Recommended citation: Ramunno, F. P., Hackstein, S., Kinakh, V., Drozdova, M., Quétant, G., Csillaghy, A., & Voloshynovskiy, S. (2024). "Solar synthetic imaging: Introducing denoising diffusion probabilistic models on SDO/AIA data." Astronomy & Astrophysics. 686(A285).
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Conference Papers