GSST/CBERS-5 International Symposium on Machine Learning in Heliophysics and Space Weather

Deep Learning-Based Automatic Detection of M and X Class Solar Flares in the Solar Disk

Aug 19, 2026, 10:00 AM

20m

Fernando de Mendonça - LIT (National Institute for Space Research, São José dos Campos, SP, Brazil)

Oral Machine Learning in Space, Earth & Atmospheric Sciences Oral Contributions

Speaker

Nicolas Campos (Universidad de Santiago de Chile (USACH))

Description

Solar flares are transient energy release events in the solar atmosphere, typically associated with magnetic reconnection in active regions. While the Geostationary Operational Environmental Satellite X-ray Sensor (GOES/XRS) provides continuous monitoring of flare activity, its lack of spatial resolution limits the identification, localization, and characterization of flaring events. In this work, we present a deep learning-based method for the automatic detection of solar flares in the central region of the solar disk, where projection effects are reduced. The model uses multi-channel observations from the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO), specifically the 304Å, 1600Å, 171Å and 131Å passbands, which sample different layers and temperature regimes of the solar atmosphere. We trained a convolutional neural network (CNN) using curated flare catalogs aligned with AIA imagery. The model is designed to detect flare events, estimate their location on the solar disk, and produce a structured description of each event. This approach provides a consistent framework for combining multi-wavelength observations with data-driven methods, with potential applications in automated monitoring and near-real-time analysis of solar activity.

Acknowledgements: We acknowledge support from ANID Chile through FONDECYT grant No. 11251905 (VAP).