Tag Archives: machine learning

156. Fast and Accurate Emulation of the SDO/HMI Stokes Inversion with Uncertainty Quantification

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.

140. Solar Flare Predictive Features Derived from Polarity Inversion Line Masks in Active Regions Using an Unsupervised Machine Learning Algorithm

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.

125. Solar Farside Magnetograms from Deep Learning Analysis of STEREO/EUVI Data

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.

25. Solar Flare Forecasting Using HMI Vector Magnetic Field Data with a Support Vector Machine Algorithm

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.