  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.
