Contributed by Jingjing Wang. Posted on October 21, 2022
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.
Contributed by Elena García Broock. Posted on June 24, 2021
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.
Contributed by Richard Higgins. Posted on April 16, 2021
An emulation of the VFISV Stokes Inversion that trains a deep
network (U-Net) to map directly from IQUV polarized light to Milne-Eddington magnetic field parameters. The accuracy of this method suggests that it could serve as a warm-start for VFISV or as a pre-disambiguation stand-in.
Contributed by Jingjing Wang. Posted on May 4, 2020
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.
Contributed by Yong-Jae Moon. Posted on April 29, 2019
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.
Contributed by C. J. Díaz Baso. Posted on June 29, 2018
A deep learning code is developed to enhance HMI continuum intensity images and line-of-sight magnetic field for a better spatial resolution.
Contributed by Kostas Florios. Posted on April 16, 2018
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.
Contributed by Xin Huang. Posted on April 11, 2018
A deep-learning method, Convolutional Neural Network, is developed to use the HMI’s line-of-sight magnetic field to forecast solar flares.
Contributed by Sebastien Couvidat. Posted on August 11, 2014
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.