activity
20242026
most citedEnhancing Image Resolution of Solar Magnetograms: A Latent Diffusion Model Approach

2 citations · 2 across the 6 of their papers we have counts for

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astro-ph.SR2026

First results from PADRE/MeDDEA: X-rays from a flare and its CME

Muriel Zoë Stiefel, Säm Krucker, Olivier Limousin +22

The MeDDEA hard X-ray spectrometer onboard NASAs PADRE CubeSat operates four flight-spare detectors from the Solar Orbiter/STIX instrument. This paper aims to evaluate the accuracy…

astro-ph.SR2026

Automated Solar Radio Burst Detection Using Deep Learning on Augmented e-Callisto Data

Vincenzo Timmel, André Csillaghy, Christian Monstein

Solar radio bursts are signatures of energetic events associated with solar flares and coronal mass ejections and can interfere with terrestrial and space-based communication syste…

astro-ph.SR2025★ 2 cited

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

Francesco Pio Ramunno, Paolo Massa, Vitaliy Kinakh +3

The spatial properties of the solar magnetic field are crucial to decoding the physical processes in the solar interior and their interplanetary effects. However, observations from…

astro-ph.SR2024

A machine learning approach for computing solar flare locations in X-rays on-board Solar Orbiter/STIX

Paolo Massa, Simon Felix, László István Etesi +7

The Spectrometer/Telescope for Imaging X-rays (STIX) on-board the ESA Solar Orbiter mission retrieves the coordinates of solar flare locations by means of a specific sub-collimator…

astro-ph.SR2024

Magnetogram-to-Magnetogram: Generative Forecasting of Solar Evolution

Francesco Pio Ramunno, Hyun-Jin Jeong, Stefan Hackstein +3

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 introduc…

astro-ph.SR2024

Solar synthetic imaging: Introducing denoising diffusion probabilistic models on SDO/AIA data

Francesco P. Ramunno, S. Hackstein, V. Kinakh +4

Given the rarity of significant solar flares compared to smaller ones, training effective machine learning models for solar activity forecasting is challenging due to insufficient…