TITLE: A Machine-Learning-Ready Dataset Prepared from the Solar and Heliospheric Observatory Mission 

ABSTRACT: 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.
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.
A Machine-Learning-Ready Dataset Prepared from the Solar and Heliospheric Observatory Mission
http://arxiv.org/abs/2108.06394v1