2021/08/04 by Carl Shneider, Shneider, Carl, Andong Hu +14
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #Gamma-ray bursts and supernovae #Geomagnetism and Paleomagnetism Studies #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Solar and Space Plasma Dynamics #Solar and Stellar Astrophysics (astro-ph.SR) #Space Physics (physics.space-ph) #astro-ph.IM #astro-ph.SR #cs.LG #physics.space-ph #stat.ML
paper · pdf · doi:10.48550/arxiv.2108.06394
under review
arxiv created 2021/08/04 · openalex publication_date 2021/08/04 · arxiv updated 2021/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a Python tool to generate a standard dataset 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) and Solar Dynamics Observatory (SDO) missions. We discuss a dataset produced from the SoHO mission's 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 Lagrange point one (L1). For this use case, we apply a deep convolutional neural network (CNN) to a subset of the full SoHO dataset and compare with baseline results from a Gaussian Naive Bayes classifier.