DeepSun: Machine-Learning-as-a-Service for Solar Flare Prediction

Yasser Abduallah1,2, Jason T. L. Wang1,2, Yang Nie1,2, Chang Liu1,3,4, Haimin Wang1,3,4

1. Institute for Space Weather Sciences, New Jersey Institute of Technology
2. Department of Computer Science, New Jersey Institute of Technology
3. Big Bear Solar Observatory, New Jersey Institute of Technology
4. Center for Solar-Terrestrial Research, New Jersey Institute of Technology

Abstract

Solar flare prediction plays an important role in understanding and forecasting space weather. The main goal of the Helioseismic and Magnetic Imager (HMI), one of the instruments on NASA's Solar Dynamics Observatory, is to study the origin of solar variability and characterize the Sun's magnetic activity. HMI provides continuous full-disk observations of the solar vector magnetic field with high cadence data that lead to reliable predictive capability; yet, solar flare prediction effort utilizing these data is still limited. In this paper, we present a machine-learning-as-a-service (MLaaS) framework, called DeepSun, for predicting solar flares on the Web based on HMI's data products. Specifically, we construct training data by utilizing the physical parameters provided by the Space-weather HMI Active Region Patches (SHARP) and categorize solar flares into four classes, namely B, C, M, X, according to the X-ray flare catalogs available at the National Centers for Environmental Information (NCEI). Thus, the solar flare prediction problem at hand is essentially a multi-class (i.e., four-class) classification problem. The DeepSun system employs several machine learning algorithms to tackle this multi-class prediction problem and provides an application programming interface (API) for remote programming users. DeepSun can be accessed here.

Datasets and Source Code

» Click here to download the source code of the machine learning algorithms described in the paper.
» Click here to download sample datasets.
» Click here to download the results obtained by running the source code on the datasets.

Reference

DeepSun: Machine-Learning-as-a-Service for Solar Flare Prediction, Abduallah, Y., Wang, J. T. L., Nie, Y., Liu, C., Wang, H., Research in Astronomy and Astrophysics, 21:160, 2021   [GitHub]