A deep convolutional neural network is trained using Hinode data that has been degraded to match the resolution and sensitivity of HMI observations. Once trained, the network can enhance HMI intensity and vector magnetic field data to the resolution and quality of Hinode.
Some magnetic features in active regions, related to strong solar flares, are considered as “anomaly” features in a machine learning algorithm. An unsupervised auto-encoder network has been trained to identify such anomalies and is used to predict occurrence of strong flares.
A neural network has been developed and applied on helioseismic far-side images, and substantially improved the number of far-side active region detections with higher true positive rate.
An unsupervised machine-learning algorithm is used on selected features derived from the polarity inversion lines (PIL) mask and difference PIL mask. It is found these features are effective in predicting flaring occurrences.
A deep learning code is trained using the Sun’s front-side observations, HMI’s magnetograms and AIA’s 304Å EUV images, to establish a relation between magnetic field and EUV flux. Then the code is applied on the STEREO/EUVI 304Å data to obtain the Sun’s far-side magnetic field.
A deep learning code is developed to enhance HMI continuum intensity images and line-of-sight magnetic field for a better spatial resolution.
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.
A deep-learning method, Convolutional Neural Network, is developed to use the HMI’s line-of-sight magnetic field to forecast solar flares.