Improved detection of farside solar active regions using deep learning

Abstract

The analysis of waves in the visible side of the Sun allows the detection of active regions in the farside through local helioseismology techniques. The knowledge of the magnetism in the whole Sun, including the non-visible hemisphere, is fundamental for several space weather forecasting applications. Seismic identification of farside active regions is challenged by the reduced signal-to-noise, and only large and strong active regions can be reliable detected. Here we develop a new methodology to improve the identification of active region signatures in farside seismic maps. We have constructed a deep neural network that associates the farside seismic maps obtained from helioseismic holography with the probability of presence of active regions in the farside. The network has been trained with pairs of helioseismic phase shift maps and Helioseismic and Magnetic Imager magnetograms acquired half a solar rotation later, which were used as a proxy for the presence of active regions in the farside. The method has been validated using a set of artificial data, and it has also been applied to actual solar observations during the period of minimum activity of the solar cycle 24. Our approach shows a higher sensitivity to the presence of farside active regions than standard methods applied up to date. The neural network can significantly increase the number of detected farside active regions, and will potentially improve the application of farside seismology to space weather forecasting.