# Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network

arXiv: https://arxiv.org/abs/2203.14393 · HTML: https://mlsanity.com/paper/2203.14393/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 47 references that are in the map.
- First posted on arXiv 27 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [Predicting Solar Flares Using a Long Short-Term Memory Network](https://mlsanity.com/paper/1905.07095/) (2019)
- [Flare Prediction Using Photospheric and Coronal Image Data](https://mlsanity.com/paper/1708.01323/) (2017)
- [Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural Networks](https://mlsanity.com/paper/2002.10953/) (2020)
- [DeepSun: Machine-Learning-as-a-Service for Solar Flare Prediction](https://mlsanity.com/paper/2009.04238/) (2020)

## Built on by (most cited first, 5 of 5)

- [Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network](https://mlsanity.com/paper/2403.18302/) (2024)
- [Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning](https://mlsanity.com/paper/2501.01011/) (2025)
- [Ensemble Learning for CME Arrival Time Prediction](https://mlsanity.com/paper/2305.00258/) (2023)
- [Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification](https://mlsanity.com/paper/2402.17196/) (2024)
- [Comparing LSTM-Based Sequence-to-Sequence Forecasting Strategies for 24-Hour Solar Proton Flux Profiles Using GOES Data](https://mlsanity.com/paper/2510.05399/) (2025)

## Abstract (arXiv)

Solar energetic particles (SEPs) are an essential source of space radiation, which are hazards for humans in space, spacecraft, and technology in general. In this paper we propose a deep learning method, specifically a bidirectional long short-term memory (biLSTM) network, to predict if an active region (AR) would produce an SEP event given that (i) the AR will produce an M- or X-class flare and a coronal mass ejection (CME) associated with the flare, or (ii) the AR will produce an M- or X-class flare regardless of whether or not the flare is associated with a CME. The data samples used in this study are collected from the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information. We select M- and X-class flares with identified ARs in the catalogs for the period between 2010 and 2021, and find the associations of flares, CMEs and SEPs in the Space Weather Database of Notifications, Knowledge, Information during the same period. Each data sample contains physical parameters collected from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Experimental results based on different performance metrics demonstrate that the proposed biLSTM network is better than related machine learning algorithms for the two SEP prediction tasks studied here. We also discuss extensions of our approach for probabilistic forecasting and calibration with empirical evaluation.