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.