Speaker
Description
Remote sensing of the Sun and the inner heliosphere remains the primary observational approach for investigating the physical mechanisms underlying the Sun’s short-term variability and its associated space weather phenomena. Given the significant societal and economic implications of space weather, a growing number of current and planned space- and ground-based observatories are dedicated to monitoring the solar atmosphere and the inner heliosphere. Over the past decade, solar physics has increasingly embraced state-of-the-art machine learning (ML) methodologies to address the challenges posed by the increasing volume, complexity, and dimensionality of scientific and operational solar data. In this talk, I present a curated compilation — assisted by artificial intelligence — of established and emerging ML applications, highlighting key trends across four major application domains:
• Data calibration (e.g., image deconvolution, super-resolution, denoising, and spectropolarimetric calibration)
• Feature classification and detection (e.g., of coronal holes, active regions, coronal mass ejections, and flare ribbons)
• Measurement and reconstruction (e.g., Stokes inversions, plasma velocity inference, coronal magnetic field extrapolations, coronal electron density mapping, and three-dimensional reconstruction of CME morphology)
• Forecasting (e.g., solar flares, solar energetic particle events, CME arrival times, sunspot number, and solar irradiance variability).