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

TL;DR

This paper introduces SolARED, a comprehensive dataset of solar active region emergence, derived from SDO/HMI observations, to support machine learning models for early prediction of solar eruptive activity.

Contribution

The paper presents a new, ML-ready dataset capturing the evolution of active regions on the Sun, enabling improved early-warning predictions of space weather events.

Findings

Dataset includes 50 active regions observed from 2010 to 2023.

Supports development of predictive models for solar active region emergence.

Available through an interactive web application.

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.