Forecasting of Solar Eruptions using Statistical Mechanics, Ensemble, and Bayesian Forecasting Methods

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9453-14-M-0148
Agency Tracking Number: F141-108-0388
Amount: $149,868.00
Phase: Phase I
Program: SBIR
Awards Year: 2014
Solicitation Year: 2014
Solicitation Topic Code: AF141-108
Solicitation Number: 2014.1
Small Business Information
20945 Great Mills Road, Suite 201, Lexington Park, MD, 20653-
DUNS: 075189415
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Greg Sanders
 Sr. Computer Scientist
 (571) 257-8403
Business Contact
 Brett Darcey
Title: Vice President, R&D
Phone: (571) 257-8403
Research Institution
Heron Systems proposes Solar Prediction via Deep Learning (SPINDLE), a human out-of-the-loop system to improve the state of solar flare forecasting using novel machine learning techniques. Currently, solar flare forecasting is either dependent on an expert, with their own subjective biases and intuitions, or automated methods using shallow representations extracted from magnetogram images, unable to learn deeper relationships in the data. SPINDLE is an automated deep learning pipeline designed to perform state-of-the-art analysis on solar observatory data for the purpose of solar flare prediction. Magnetogram and other data are collected from observatories, pre-processed, and then fed into the deep learning prediction pipeline for classification of X, M, and C solar flares in 6, 12, and 24 hour time windows. Deep learning enables the system to automatically learn sub-structures within image data over time-series, with the potential to not only dramatically improve forecasting itself, but also advance our understanding of the underlying mechanisms in solar flares. To demonstrate feasibility, we will benchmark the system against NOAA forecasts, as well as reported results in the solar flare machine learning literature.

* Information listed above is at the time of submission. *

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