Solar Active Region Magnetogram Image Dataset for Studies of Space Weather

Abstract

In this dataset we provide a comprehensive collection of magnetograms (images quantifying the strength of the magnetic field) from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions (regions of large magnetic flux, generally the source of eruptive events) as well as labels of corresponding flaring activity. This dataset will be useful for image analysis or solar physics research related to magnetic structure, its evolution over time, and its relation to solar flares. The dataset will be of interest to those researchers investigating automated solar flare prediction methods, including supervised and unsupervised machine learning (classical and deep), binary and multi-class classification, and regression. This dataset is a minimally processed, user configurable dataset of consistently sized images of solar active regions that can serve as a benchmark dataset for solar flare prediction research.

References in corpus (26)

• The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: SHARPs -- Space-weather HMI Active Region Patches
• Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm
• Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning
• Deep Flare Net (DeFN) model for solar flare prediction
• Predicting Solar Flares Using a Long Short-Term Memory Network
• Observables Processing for the Helioseismic and Magnetic Imager Instrument on the Solar Dynamics Observatory
• Predicting Solar Flares Using SDO/HMI Vector Magnetic Data Product and Random Forest Algorithm
• A Comparison of Flare Forecasting Methods. II. Benchmarks, Metrics and Performance Results for Operational Solar Flare Forecasting Systems
• Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters
• Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data
• Predicting solar flares with machine learning: investigating solar cycle dependence
• Feature ranking of active region source properties in solar flare forecasting and the uncompromised stochasticity of flare occurrence
• Solar Flare Intensity Prediction with Machine Learning Models
• Prediction of Solar Flares Using Unique Signatures of Magnetic Field Images
• Prediction of Solar Flare Size and Time-to-Flare Using Support Vector Machine Regression
• Operational solar flare prediction model using Deep Flare Net
• An automated classification approach to ranking photospheric proxies of magnetic energy build-up
• The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Magnetohydrodynamics Simulation Module for the Global Solar Corona
• Relationships Between Characteristics of the Line-of-Sight Magnetic Field and Solar Flare Forecasts
• Evaluating (and Improving) Estimates of the Solar Radial Magnetic Field Component from Line-of-Sight Magnetograms
• Fine-grained Solar Flare Forecasting Based on the Hybrid Convolutional Neural Networks
• Supervised convolutional neural networks for classification of flaring and nonflaring active regions using line-of-sight magnetograms
• Decreasing False Alarm Rates in ML-based Solar Flare Prediction using SDO/HMI Data
• A Framework for Designing and Evaluating Solar Flare Forecasting Systems
• Machine Learning Approaches to Solar-Flare Forecasting: Is Complex Better?
• Solar Active Region Magnetogram Image Dataset for Studies of Space Weather

Cited by in corpus (4)

• Solar Active Region Magnetogram Image Dataset for Studies of Space Weather
• Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition
• Solar flare forecasting with foundational transformer models across image, video, and time-series modalities
• Bypassing the static input size of neural networks in flare forecasting by using spatial pyramid pooling