103. Enhancing SDO/HMI Images Using Deep Learning
A deep learning code is developed to enhance HMI continuum intensity images and line-of-sight magnetic field for a better spatial resolution.
Tag Archives: machine learning
95. Forecasting Solar Flares Using Magnetogram-Based Predictors and Machine Learning
A set of parameters that characterize the complexity and energy potential of solar active-regions is fed through several Machine Learning and conventional statistics algorithms to forecast solar flares.
94. Deep Learning Based Solar Flare Forecasting Model: Results for Line-of-Sight Magnetograms
A deep-learning method, Convolutional Neural Network, is developed to use the HMI’s line-of-sight magnetic field to forecast solar flares.
25. Solar Flare Forecasting Using HMI Vector Magnetic Field Data with a Support Vector Machine Algorithm
We attempt to forecast M- and X-class solar flares using a support vector machine algorithm and 4 years of HMI vector magnetic field data. With the true skill statistic as the preferred metric to estimate the algorithm performances, we obtain good predictive abilities. This may be partly due to fine-tuning the algorithm for this purpose, and to a choice of 13 SHARP parameters obtained from vector magnetograms.