Supervised neural networks for helioseismic ring-diagram inversions

Context. The inversion of ring fit parameters to obtain subsurface flow maps in ring-diagram analysis for eight years of SDO observations is computationally expensive, requiring ∼3200 CPU hours. Aims. In this paper we apply machine-learning techniques to the inversion step of the ring diagram pipeline in order to speed up the calculations. Specifically, we train a predictor for subsurface flows using the mode fit parameters and the previous inversion results to replace future inversion requirements. Methods. We utilize artificial neural networks (ANNs) as a supervised learning method for predicting the flows in 15° ring tiles. We discuss each step of the proposed method to determine the optimal approach. In order to demonstrate that the machine-learning results still contain the subtle signatures key to local helioseismic studies, we use the machine-learning results to study the recently discovered solar equatorial Rossby waves. Results. The ANN is computationally efficient, able to make future flow predictions of an entire Carrington rotation in a matter of seconds, which is much faster than the current ∼31 CPU hours. Initial training of the networks requires ∼3 CPU hours. The trained ANN can achieve a rms error equal to approximately half that reported for the velocity inversions, demonstrating the accuracy of the machine learning (and perhaps the overestimation of the original errors from the ring-diagram pipeline). We find the signature of equatorial Rossby waves in the machine-learning flows covering six years of data, demonstrating that small-amplitude signals are maintained. The recovery of Rossby waves in the machine-learning flow maps can be achieved with only one Carrington rotation (27.275 days) of training data. Conclusions. We show that machine learning can be applied to and perform more efficiently than the current ring-diagram inversion. The computation burden of the machine learning includes 3 CPU hours for initial training, then around 10−4 CPU hours for future predictions.

Whole Heliosphere Interval: Overview of JD16

The Whole Heliosphere Interval is an international observing and modeling effort to characterize the three-dimensional interconnected solar-heliospheric-planetary system, i.e., the “heliophysical” system. WHI was part of the International Heliophysical Year, on the 50th anniversary of the International Geophysical Year, and benefited from hundreds of observatories and instruments participating in IHY activities. WHI describes the 3-D heliosphere originating from solar Carrington Rotation 2068, March 20–April 16, 2008. The focus of IAU JD16 was on analyses of observations obtained during WHI, and simulations and modeling involving those data and that period. Consideration of the WHI interval in the context of surrounding solar rotations and/or compared to last solar minimum was also encouraged. Our goal was to identify connections and commonalities between the various regions of the heliosphere.

A Summary of 3-D Reconstructions of the Whole Heliosphere Interval and Comparison with in-Ecliptic Solar Wind Measurements from STEREO, ACE, and Wind Instrumentation

We present a summary of results from simultaneous Solar-Terrestrial Environment Laboratory (STELab) Interplanetary Scintillation (IPS), STEREO, ACE, and Wind observations using three-dimensional reconstructions of the Whole Heliosphere Interval – Carrington rotation 2068. This is part of the world-wide IPS community's International Heliosphysical Year (IHY) collaboration. We show the global structure of the inner heliosphere and how our 3-D reconstructions compare with in-ecliptic spacecraft measurements.