AI Space
Model Adds Far-Side Solar Activity to Sun Maps
A probabilistic method aims to screen false detections before they enter models of the Sun’s magnetic field.

Illustration of the Sun with magnetic-field lines and wave patterns suggesting observations of its far side.
Photo: Kronos News
A Southwest Research Institute-led team has developed a method to include probable active regions on the Sun’s far side in models of its magnetic surface. The approach assigns probabilities to helioseismic detections before they are added, aiming to reduce false alarms known as “ghost active regions.” [1]
The team studied near-simultaneous maps of the whole solar surface from 2010 to 2014, combining observations from Earth with data showing the Sun’s far side. These surface-flux-transport models track how magnetic fields move across the Sun and are used in space-weather research. [1]
The institute says the method offers a practical way to incorporate far-side detections into these models. The study was published in The Astrophysical Journal Supplement Series; the available source is the institute’s announcement, so the reported improvements are not independently assessed here. [1]
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Only the Southwest Research Institute announcement was accessible; the second supplied source could not be verified.
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Kronos News Desk covers ai space and editorial analysis for Kronos News.



