SolARED: Solar Active Region Emergence Dataset for Machine Learning Aided Predictions

2026-01-19

AI Summary

To enhance early prediction of solar eruptive activity, this work presents the SolARED dataset, which focuses on the pre-emergence evolution of solar active regions (ARs). Leveraging multi-channel observations from SDO/HMI, the dataset integrates time-series measurements of multiple physical parameters—including acoustic power, magnetic flux, and continuum intensity—through systematic application of image remapping, target tracking, and spatial binning techniques. It provides a standardized, machine learning–ready collection covering 50 major active regions and their surrounding areas from 2010 to 2023. SolARED is the first structured dataset to capture the multi-parameter evolution prior to AR emergence, addressing a critical gap in data for early-warning solar forecasting. The dataset has been publicly released via an interactive platform to support operational space weather prediction research.

Technology Category

• Machine Learning: Active Learning
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The development of accurate forecasts of solar eruptive activity has become increasingly important for preventing potential impacts on space technologies and exploration. Therefore, it is crucial to detect Active Regions (ARs) before they start forming on the solar surface. This will enable the development of early-warning capabilities for upcoming space weather disturbances. For this reason, we prepared the Solar Active Region Emergence Dataset (SolARED). The dataset is derived from full-disk maps of the Doppler velocity, magnetic field, and continuum intensity, obtained by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). SolARED includes time series of remapped, tracked, and binned data that characterize the evolution of acoustic power of solar oscillations, unsigned magnetic flux, and continuum intensity for 50 large ARs before, during, and after their emergence on the solar surface, as well as surrounding areas observed on the solar disc between 2010 and 2023. The resulting ML-ready SolARED dataset is designed to support enhancements of predictive capabilities, enabling the development of operational forecasts for the emergence of active regions. The SolARED dataset is available at https://sun.njit.edu/sarportal/, through an interactive visualization web application.

Problem

• solar active regions
• early detection
• space weather forecasting
• solar eruption prediction
• machine learning

Innovation

• Solar Active Region
• Machine Learning Dataset
• Helioseismic and Magnetic Imager
• Space Weather Forecasting
• Acoustic Power