Convolutional neural networks for predicting the strength of the near-earth magnetic field caused by interplanetary coronal mass ejections

We then allow the flux rope to expand and record max{−Bz} as the ICME passes 1AU. We further conduct two separate machine learning experiments and develop two different regression-based deep convolutional neural networks (CNNs) to predict max{−Bz} based on these two kinds of the near-Sun input data. Experiment 1 is a test which we do as a proof of concept, to see if a 3-channel CNN (hereafter CNN1), similar to those used in RGB image recognition, can reproduce the results of the self-similar (i.e., scale-invariant) expansion of the G&L model.

Experiment 2 is less trivial, as Stokes vector is not linearly related to B, and the line-of-sight integration in the optically thin corona presents additional difficulties for interpreting the signal. This second CNN (hereafter CNN2), although resembling CNN1 in Experiment 1, will have a different number of layers and set of hyperparameters due to a much more complicated mapping between the input and output data. We find that, given three components of B, CNN1 can predict max{−Bz} with 97% accuracy, and for three components of the Stokes vector as input, CNN2 can predict max{−Bz} with 95%, both measured in the relative root square error.