The origin and injection of seed particles in the acceleration and transport of solar energetic particles (SEPs) in the Sun?s corona and the solar wind is still a poorly understood problem in heliophysics, thus making it a formidable task to predict with accuracy the SEP flux for the sake of reliable space weather forecast. This 3-year project aims to investigate the distribution of seed population in the solar corona and its influence in the production of SEPs. The project team will develop a Machine Learning (ML) model for the prediction of SEP events, which will be based on the Energetic Particle Radiation Environment Module (EPREM) code developed at the University of New Hampshire. The model will utilize data from the GOES, STEREO, and the Parker Solar Probe satellites, and it will adopt open-source ML libraries. This project is expected to advance our understanding of the origin and distribution of seed particles in the Sun?s corona, which influence the acceleration and transport of SEPs throughout the heliosphere. The project will develop the necessary technique and an algorithm for surrogate models that will have diverse applications in space weather forecasting. The research investigations, led by a mid-career female PI, will involve graduate students at the University of New Hampshire. The research and EPO agenda of this project supports the Strategic Goals of the AGS Division in discovery, learning, diversity, and interdisciplinary research.
The challenge in developing a ML model for an accurate SEP prediction is the lack of sufficient database of observed SEP events to train and validate the ML model, which is known as the ?class imbalance? problem. One way to circumvent this difficulty is to employ surrogate models: that is, train the ML model on synthetic/simulated data of SEP events, and then optimize and validate the model using the observed SEP data. During this 3-year project, the team will simulate major SEP events using the EPREM code in order to explore the parameter space for the seed population; they will consider the pre-, during, and post-event spectra of the events selected for the study. The project team will also simulate time-series of SEP fluxes by incorporating these seed population parameters in order to train the ML model and then use the available SEP events database for testing. The project will make use of deep learning techniques such as LSTM as well as classification and dimensionality reduction techniques such as PCA, tSNE and autoencoder.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
