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Found 7 papers, 1 papers with code
Probabilistic solar flare forecasting using historical magnetogram data
In this paper, we consider over 4 solar cycles of daily historical magnetogram data from multiple instruments.
Efficient labeling of solar flux evolution videos by a deep learning model
Machine learning (ML) is becoming a critical tool for interrogation of large complex data.
Homogenising SoHO/EIT and SDO/AIA 171Å$~$ Images: A Deep Learning Approach
Extreme Ultraviolet images of the Sun are becoming an integral part of space weather prediction tasks.
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks
We harness the power of deep learning and successfully apply Convolutional Neural Networks to regress stellar rotation periods from Kepler light curves.
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties
Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient.
Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses
Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation.
A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission
In this paper we present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research.
