Category Archives: Machine Learning

212. Deconvolving SDO/HMI Intensity and Vector Magnetic Field Data to Achieve Hinode/SOT-SP Quality

Contributed by David Korda. Posted on May 19, 2025

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.

187. Precursor Identification for Strong Flares Based on Anomaly Detection Algorithm

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.

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.