A Machine Learning Dataset Prepared from the SoHO Mission for Space Weather Readiness
Abstract
We present a Python tool to generate a standard data set from solar images that allows for user-defined selection criteria and a range of pre-processing steps. Our Python tool works with all image products from both the Solar and Heliospheric Observatory (SoHO) mission as well as the Solar Dynamics Observatory (SDO) mission. We discuss a data set produced from the SoHO missions multi-spectral images which is free of missing or corrupt data as well as planetary transits in coronagraph images, and is temporally synced making it ready for input to a machine learning system. Machine-learning-ready images are a valuable resource for the community because they can be used, for example, for forecasting space weather parameters. We illustrate the use of this data with a 3-5 day-ahead forecast of the north-south component of the interplanetary magnetic field (IMF) observed at L1. We apply a deep convolutional neural network (CNN) to a subset of the full SoHO data set and compare with baseline results from a Gaussian Naive Bayes classifier.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFMSA11A..03H